A survey on ground segmentation methods for automotive LiDAR sensors

Ci a ion: Gomes, T.; Ma ias, D.; Campos, A.; Cunha, L.; Ro iz, R. A Su ey on G ound Segmen a ion Me hods o Au omo i e LiDAR Senso s. Senso s 2023,23, 601. h ps://doi.o g/10.3390/s23020601 Academic Edi o s: I-Hsi Kao, Yi-Ho ng Lai, Jau-Woei Pe ng and Ching-Yao Chan Recei ed: 8 Decembe 2022 Re ised: 27 Decembe 2022 Accep ed: 29 Decembe 2022 Published: 5 Janua y 2023 Copy igh : © 2023 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps:// c ea i ecommons.o g/licenses/by/ 4.0/). senso s Re iew A Su ey on G ound Segmen a ion Me hods o Au omo i e LiDAR Senso s Tiago Gomes * , Diogo Ma ias , And é Campos , Luís Cunha and Rica do Ro iz Cen o ALGORITMI/LASI, Escola de Engenha ia, Uni e sidade do Minho, 4800-058 Guima aes, Po ugal *Co espondence: m [email p o ec ed] Abs ac : In he nea u u e, au onomous ehicles wi h ull sel -d i ing ea u es will popula e ou public oads. Howe e , ully au onomous ca s will equi e obus pe cep ion sys ems o sa ely na iga e he en i onmen , which includes came as, RADAR de ices, and Ligh De ec ion and Ranging (LiDAR) senso s. LiDAR is cu en ly a key senso o he u u e o au onomous d i ing since i can ead he ehicle’s icini y and p o ide a eal- ime 3D isualiza ion o he su oundings h ough a poin cloud ep esen a ion. These ea u es can assis he au onomous ehicle in se e al asks, such as objec iden i ica ion and obs acle a oidance, accu a e speed and dis ance measu emen s, oad na iga ion, and mo e. Howe e , i is c ucial o de ec he g ound plane and oad limi s o sa ely na iga e he en i onmen , which equi es ex ac ing in o ma ion om he poin cloud o accu a ely de ec common oad bounda ies. This a icle p esen s a su ey o exis ing me hods used o de ec and ex ac g ound poin s om LiDAR poin clouds. I summa izes he al eady ex ensi e li e a u e and p oposes a comp ehensi e axonomy o help unde s and he cu en g ound segmen a ion me hods ha can be used in au omo i e LiDAR senso s. Keywo ds: au onomous d i ing; LiDAR; pe cep ion sys em; g ound segmen a ion; su ey 1. In oduc ion In he e y nea u u e, au onomous ehicles wi h ull sel -d i ing ea u es will ci cula e on ou public oads [ 1 ]. None heless, mos in elligen ehicles oday a e s ill manually con olled, co esponding o he i s h ee le els o d i ing au oma ion de ined by he socie y o au omo i e enginee s (SAE), i.e., he six le els o au oma ion equi ed o each ull d i ing au oma ion ea u es. Acco ding o he SAE J3016 s anda d ( e . 202104) [ 2 ], in le els 0, 1, and 2, he d i e mus ac i ely moni o he d i ing ac i i ies, while in le els 3, 4, and 5, he au oma ed ehicle should be able o moni o and na iga e he en i onmen , au onomously. Despi e cu en ca s suppo ing only ea u es up o SAE-le el 2, e.g., mode n ad anced d i e -assis ance sys ems (ADAS) can al eady p o ide pa ial ehicle’s au oma ion, ca manu ac u e s a e only now ecei ing app o als o SAE-le el 3 [ 3 , 4 ], which de ines explici ly ha he au oma ed d i ing unc ion can ake o e ce ain d i ing asks. Howe e , a d i e is s ill equi ed and mus be eady o ake con ol o he ca a all imes when p omp ed o in e ene by he ehicle. Achie ing sa e au oma ed d i ing ea u es equi es obus and eliable pe cep ion sys ems [ 5 ] ha depend on mul iple senso se ups o na iga e he en i onmen , which usually include came as, RADAR de ices, and Ligh De ec ion and Ranging (LiDAR) senso s [ 6 – 8 ]. Fo ins ance, he Ge man Fede al Mo o T anspo Au ho i y (KBA) has inally g an ed he i s SAE-le el 3 and UN Regula ion numbe 157 [ 4 ] app o al o Me cedes-Benz [ 3 ], whose pe cep ion sys em includes a we ness senso in he wheel, LiDAR senso s, mic ophones, and a came a in he ea window, p ima ily used o de ec ing blue ligh s and o he special signals om eme gency ehicles. This echnical app o al was mainly possible due o he adop ion o LiDAR o na iga e he en i onmen . A LiDAR senso wo ks by i ing a lase pulse o a a ge and cap u ing he e lec ed signal, whe e he dis ance o he a ge is ob ained by calcula ing he ound- ip ime o he Senso s 2023,23, 601. h ps://doi.o g/10.3390/s23020601 h ps://www.mdpi.com/jou nal/senso s Senso s 2023,23, 601 2 o 30 a eling ligh . I s ou pu is a 3D poin cloud ha can be used o e ain mapping and ec e- a ing he su ounding en i onmen . Thus, his echnology has become widely popula in se e al ai bo ne lase scanning (ALS) applica ions, such as a chaeology [9–11] , geology [ 12 ], o es y [ 13 , 14 ], geog aphy and opog aphy [ 14 – 18 ], su eying [ 14 ], lase al ime y [ 19 ], and many mo e [ 14 ]. Due o i s wide success in such domains, LiDAR senso s ecen ly s a ed o be adop ed by he au omo i e indus y in he pe cep ion sys em o he ca . Thei 3D poin cloud can be e y use ul in se e al au onomous d i ing applica ions [20–22] , such as obs a- cles, objec s, and ehicles de ec ion [ 23 – 26 ]; pedes ians ecogni ion and acking [ 27 , 28 ]; g ound segmen a ion o oad de ec ion and na iga ion [29] ; among o he s [ 30 ]. Because his echnology wo ks wi h ac i e illumina ion, LiDAR senso s allow ound- he-clock obse a ions, p o iding accu a e measu emen s o he ehicle’s icini y up o hund eds o me e s. Howe e , se e al challenges may a ec he p ocessing o he ecei ed poin cloud such as LiDAR mu ual in e e ence [ 31 – 33 ], and ad e se wea he [34–38] . Addi ionally, because a high- esolu ion senso can p oduce a conside able amoun o da a, e.g., he Velodyne senso VLS-128 can ou pu up o 9.6M poin s pe second, i is impo an o handle he poin cloud be o e being deli e ed o high-le el applica ions, bo h in e ms o packe handling [39], da a comp ession [40,41], and poin cloud denoising [36]. Rega ding au onomous na iga ion capabili ies, he ehicle mus pe cei e he su - oundings by unde s anding he loca ion and shape o he d i ing en i onmen . Thus, oad bounda ies, such as cu bs, asphal be ms, walls, and o he geome ic ea u es, a e ypical oad cha ac e is ics ha he pe cep ion sys em mus au oma ically de ec o na iga e he en i onmen sa ely [ 42 ]. Howe e , de ec ing such ea u es equi es he pe cep ion sys em o pe o m g ound and objec segmen a ion echniques o e icien ly iden i y o emo e undesi ed objec da a [ 23 , 43 – 45 ]. Because his opic has been ho oughly s udied in he las ew yea s, he e is a ple ho a o g ound segmen a ion me hods in he li e a u e ha can be used o ex ac oad bounda y in o ma ion om he poin cloud. Because such me hods can ollow di e en app oaches, he e a e se e al ade-o s, e.g., accu acy, pe o mance, compu ing equi emen s, e c., ha mus be conside ed be o e choosing he bes and he mos app op ia e me hod o deploy in he pe cep ion sys em. To help in unde s anding he exis ing g ound segmen a ion me hods in he li e a u e, he main con ibu ions o his a icle a e as ollows: (1) a li e a u e e iew on s a e-o - he-a app oaches o de ec and ex ac g ound poin s om LiDAR da a, applied in an au omo i e scena io; (2) a comp ehensi e axonomy p oposal wi h i e high-le el ca ego ies, ollowed by a discussion o he echnical aspec s and algo i hms o he app oaches ha can cu en ly be ound in he li e a u e; (3) and a quali a i e compa ison be ween he di e en ca ego ies ega ding impo an me ics, including eal- ime, esou ce equi emen s, and he algo- i hm’s pe o mance in di e en en i onmen s and aspec s. The emainde o his a icle is o ganized as ollows: Sec ion 2p esen s he concep s and applica ions behind LiDAR, while Sec ion 3shows cu en g ound segmen a ion me hods and p oposed axonomy. Sec ion 4 discusses and gi es a quali a i e compa ison be ween he s a e-o - he-a con ibu ions. Finally, Sec ion 5concludes his a icle. 2. Au omo i e LiDAR Senso s Fo he las 20 yea s, au onomous d i ing has been a well-es ablished esea ch opic. Howe e , despi e some p o o ypes being al eady a ailable and unning, he au omo i e indus y is only now s a ing o p o ide comme cial p oduc s, which will soon u n au- onomous d i ing ehicles mains eam. Aiming a eaching SAE-le el 5, ehicles a e being equipped wi h se e al ADAS ea u es, which a e cons an ly being imp o ed e e y yea . This pushes esea ch o ad ance in di e en sensing echnologies and in c ea ing mo e obus pe cep ion sys ems. Figu e 1depic s a ypical mul i-senso pe cep ion sys em com- posed o came as, RADAR, and LiDAR senso s, which can help in p o iding c oss- a ic ale s, blind spo assis [ 46 ], Adap i e C uise Con ol (ACC) [ 47 ], pedes ian de ec ion and a oidance [ 48 ], and Au oma ic Eme gency B aking (AEB) [ 49 , 50 ]. Toge he , all hese sen- so s allow e ie ing edundan in o ma ion abou he su ounding en i onmen , ensu ing Senso s 2023,23, 601 3 o 30 ha high-le el au onomous d i ing decisions a e made based on accu a e ep esen a ions o he ehicle’s icini y. Figu e 1. Mul i-senso pe cep ion sys em. Among all senso s, LiDAR is becoming he mos impo an in au onomous appli- ca ions as i can p o ide up o 300 m o eal- ime 3D in o ma ion abou he ehicle’s su oundings. Compa ed wi h RADAR, which sha es he same wo king p inciple o mea- su ing dis ances based on he ound- ip ime o an emi ed signal, LiDAR can ope a e in much highe equencies due o he ligh p ope ies. Mo eo e , while RADAR senso s only p o ide angula esolu ions o a mos 1 deg ee, which is insu icien o objec shape es ima ion [ 51 ], LiDAR senso s can achie e esolu ions o en hs o deg ees. Thus, hei ou pu can be used o objec segmen a ion a he han solely objec de ec ion. None heless, because LiDAR senso s use ligh , hey s ill p esen disad an ages when compa ed wi h adio-based senso s. Since ligh has high abso p ion in wa e , ad e se wea he condi ions, such as hea y ain all, can a ec he o e all pe o mance o LiDAR. On he o he hand, he highe wa eleng hs used in RADAR can p esen good pe o mance in poo wea he , making his senso capable o co e ing longe dis ances bu wi h less emi ed powe . Rega ding he esolu ion o he ou pu da a, came as can achie e colo ed and high- esolu ion in o ma ion abou he d i ing en i onmen . Howe e , hey a e subjec o se e al ligh - ela ed issues, e.g., hey canno co ec ly wo k a nigh o in he p esence o in ense ligh sou ces, which can u n he segmen a ion asks elying solely on came as qui e challenging. Addi ionally, dep h in o ma ion abou he en i onmen is only possible wi h s e eo came as and u he image p ocessing, which a ec s he dis ance measu emen s. 2.1. LiDAR Technology The e is a ple ho a o LiDAR senso s cu en ly a ailable on he ma ke [ 21 , 52 ]. Despi e all sha ing he same ligh -based ope a ion p ope ies, manu ac u e s al eady p o ide di e en measu emen and imaging sys ems app oaches, which ansla es in o di e en pe o mance me ics and o e all cos s. As depic ed in Figu e 2, a LiDAR senso uses a ligh signal o measu e dis ances, which a e calcula ed based on he ound- ip delay ( τ ) be ween a signal emi ed by a lase and e lec ed by a a ge . Since he speed o ligh ( c ) is p e iously known, he dis ance (R) o he a ge is calcula ed using Equa ion (1). R= 1 2cτ(1) Senso s 2023,23, 601 4 o 30 T ansmi ed Signal Backsca e ed Signal Emi e Recei e LiDAR RANGE ToF TARGET Figu e 2. LiDAR wo king p inciple. 2.1.1. Measu emen Techniques Depending on he senso ’s echnology, he ound- ip delay, also known as he Time- o -Fligh (ToF), can be measu ed h ough di e en echniques [ 53 , 54 ]: pulsed, Ampli ude Modula ed Con inuous Wa e (AMCW), and F equency Modula ed Con inuous Wa e (FMCW). Measu emen echniques based on pulsed signals a e mo e s aigh o wa d o implemen as hey only equi e accu a e ime s o Time- o-Digi al Con e e s (TDC) o di ec ly measu e and calcula e he ToF. Due o hei simplici y, hey o e a low-cos and small-size implemen a ion bu a e mo e subjec o low Signal-Noise Ra io (SNR), which limi s he accu acy o he measu emen s. Wi h AMCW and FMCW, ins ead o sending sho high-in ensi y pulses, he senso emi s a modula ed con inuous wa e signal, consequen ly achie ing be e SNR alues. In an AMCW sys em, he senso emi s a wa e synch onized wi h a se ies o in eg a ing windows. Since he e lec ed wa e is expec ed o be ou o phase, he dis ance can be calcula ed based on he ene gy a io p esen on each window: sho dis ances esul in a e lec ed signal mo e p esen in he i s windows, while o long dis ances, he e lec ed signal can be ound in he las windows. Simila ly o pulsed-based senso s, AMCW can also p o ide a simple design. How- e e , long- ange de ec ion equi es longe wa eleng hs o ensu e ha he phase di e ence be ween signals does no exceed he signal’s pe iod, which would p oduce dis ance ambi- gui y. The e o e, hei applica ion is limi ed mainly o mid- and sho - ange senso s due o eye sa e y egula ions. On he o he hand, FMCW sys ems modula e he emi ing signal equency wi h an up-chi p signal ha inc eases i s equency o e ime. Then, he senso compu es he di e ence be ween he equency o he e u n signal and a local oscilla o , being his del a di ec ly p opo ional o he dis ance o he a ge . Despi e p o iding mo e obus ness o ex e nal ligh sou ces and e en allowing o di ec ly e ie e he speed o a mo ing a ge due o Dopple shi , he inc ease in op ical componen s makes FMCW-based senso s mo e expensi e o build. 2.1.2. Imaging Techniques To c ea e a 3D poin cloud ep esen a ion in eal ime, LiDAR senso s emi and collec signals ac oss mul iple di ec ions wi hin hei suppo ed Field o View (FoV), which can esul in a poin cloud holding millions o poin s. This is achie ed by using di e en imaging echniques [ 55 ], such as solid-s a e, o o -based, and lash-based senso s. Velo- dyne pionee ed o o -based LiDAR senso s. By ha ing a mechanical sys em ha spins he scanning pa , hey can c ea e a 360 º ho izon al FoV, while he numbe o exis ing emi - e / ecep o pai s, also known as channels, de ine he e ical FoV. This echnology was so success ul ha oday i is he mos used in LiDAR, which esul ed in se e al manu ac u e s compe ing in his ma ke niche. Howe e , hey s ill come wi h some limi a ions, such as p ice, bulkiness, and he educed ame a e caused by mechanical pa s. Rega ding he exis ing solid-s a e scanning solu ions, some echnologies, e.g., mi o MEMS [ 56 ] and Op ical Phased A ay (OPA) [ 57 ], a e being deployed o eplace he bulky Senso s 2023,23, 601 5 o 30 o a ional pa s exis ing in o o -based senso s. Despi e achie ing as e scanning equen- cies and o e ing a be e o e all design o mass p oduc ion (which educes he o e all p oduc p ice), he senso ’s ange is s ill a limi a ion due o powe es ic ions in he lase uni . Addi ionally, o applica ions equi ing la ge FoV, mul iple senso s mus be a ached o he se up o gua an ee he ull co e age o he su ounding en i onmen . Aiming a educing he limi a ions c ea ed by complex bulky, o iny s ee ing sys ems, a lash-based LiDAR senso does no equi e any s ee ing sys em o emi lase signals ac oss he FoV. Ins ead, i uses a lash o ligh o illumina e he en i e en i onmen while pho o-de ec o s collec he back-sca e ed ligh . By simul aneously sha ing he ligh sou ce ac oss all he FoV, cap u ing da a esul s in a as e ope a ion, making hese senso s highly immune o ligh dis o ion [ 58 ]. Howe e , despi e he simplici y o he emi e , he ecei e sys em is qui e complex since i mus be able o di e en ia e he e u ning ligh om each poin . In o de o gua an ee a sui able senso spa ial esolu ion, hese senso s equi e high-densi y pho o-de ec ion a ays, which makes hem mo e expensi e when compa ed wi h o he solid-s a e solu ions. 2.2. LiDAR Applica ions LiDAR is a key senso in a pe cep ion sys em since i s ou pu can be used o imp o ing au onomous d i ing asks based on senso usion p ocessing [ 59 ], e.g., objec de ec ion and segmen a ion o objec classi ica ion and collision a oidance [ 23 – 25 ], Simul aneous Localiza ion and Mapping (SLAM) applica ions [ 60 ], he de ec ion and na iga ion o he d i able a ea [ 61 , 62 ], and much mo e. The LiDAR ou pu is a poin cloud, which is an exac 3D image o he en i onmen cap u ed by he senso . Humans do no easily unde s and i s in o ma ion a i s sigh , bu algo i hms can do a g ea job o in e p e ing i s con en . 2.2.1. Objec De ec ion and Classi ica ion The e a e many me hods and algo i hms o de ec ing and classi ying objec s in poin cloud da a, bu i s , i is necessa y o ind hem. A e con e ing aw da a in o a poin cloud s uc u e, one o he i s s eps is he poin clus e ing o segmen a ion, which basically consis s in g ouping poin s based on common cha ac e is ics [ 23 ]. A e his s ep, edundan da a can be il e ed/ emo ed om he poin cloud, esul ing in less da a o be ans e ed and p ocessed in he upcoming phases. In applica ions whe e he senso keeps a s a iona y posi ion, some algo i hms s a by classi ying he poin cloud in o backg ound and o eg ound da a [ 24 , 25 ]. Poin s ha sha e he same posi ion ac oss mul iple ames a e conside ed backg ound, being disca ded as hey do no ep esen dynamic objec s. Fo he emaining poin s ( o eg ound), he dis ance be ween poin s is measu ed, and poin s close o each o he a e clus e ed and ma ked wi h a bounding box as hey possibly ep esen an objec . Howe e , when he senso mo es wi h he ca , hese app oaches a e no e ec i e, as he backg ound and objec s mo e oge he inside he poin cloud. The e o e, au omo i e app oaches equi e obus and as e algo i hms since he objec s in he poin cloud also change a highe equencies. Fi s app oaches applied hand-c a ed ea u es and sliding windows algo i hms wi h Suppo Vec o Machine (SVM) classi ie s o objec iden i ica ion, bu soon we e eplaced by o he imp o ed me hods such as 2D ep esen a ions, olume ic-based, and aw poin -based da a, which deploy machine lea ning echniques in he pe cep ion sys em o he ehicle [28]. 2.2.2. SLAM SLAM, a well-es ablished esea ch opic in he ield o obo ics ha s udies solu ions o build eal- ime localiza ion maps based solely on pe cep ion da a, has also been p oposed o be applied in au onomous ehicle applica ions, e en despi e he conside able amoun o 3D in o ma ion he LiDAR senso gene a es. Usually, odome y uses da a om se e al senso s, e.g., IMUs and came as, o es ima e he ehicle’s posi ion ela i e o a s a ing loca ion. Howe e , because LiDAR senso s can gene a e high da a a es, some app oaches ollow he end o LOAM [ 60 ], which p ocesses he odome y ask a a highe equency, and Senso s 2023,23, 601 6 o 30 he mapping a a much lowe a e o keep he eal- ime equi emen s. Despi e p o iding good esul s, hey end o su e om p oblems associa ed wi h accumula ed d i . To minimize his issue, some me hods apply an ex a e-localiza ion s ep, ei he using o line map in o ma ion o imp o e he posi ion es ima ion, o by combining da a om o he senso s. Some lea ning-based app oaches a e also eme ging, which can use a pipeline o Con olu ional Neu onal Ne wo ks (CNNs) o manage local ea u e desc ip o s, in e he localiza ion o se , and apply empo al smoo hness due o he sequen ial na u e o he localiza ion ask. These equi e, howe e , mo e compu a ional equi emen s. 2.2.3. D i able A ea De ec ion In au onomous ehicle na iga ion, de ec ing he d i able a ea is one o he mos c i ical asks. Fo he ca o sa ely mo e in he en i onmen , i is necessa y he de ec ion o no only obs acles such as pedes ians o o he ehicles bu also se e al oad ea u es such as oad bounda ies, i.e., cu bs, asphal be ms, walls and o he geome ic ea u es, c osswalks, sidewalks, a ic signs, e c. [ 61 , 62 ]. Since LiDAR senso s can p o ide in ensi y in o ma ion in he poin cloud, i becomes easie o iden i y high- e lec ion oad elemen s such as a ic signs and oad ma king pain . None heless, o educe da a ans e , i is also necessa y o dis inguish backg ound om o eg ound da a, including g ound. By classi ying poin cloud da a in o g ound and non-g ound poin s, he d i able a ea can be de ec ed mo e e icien ly since he e a e less poin s o p ocess, which imp o es he na iga ion ea u es o he au onomous ca while keeping he eal- ime equi emen s. 3. G ound Segmen a ion Me hods G ound segmen a ion me hods ha e been ho oughly s udied in he li e a u e since hey play a c ucial ole in di e en au onomous d i ing asks. Wi h he e olu ion o LiDAR and i s endless applica ions, se e al algo i hms and app oaches ha e eme ged h oughou he yea s. Figu e 3depic s he adi ional da a low o a classical 3D objec de ec ion s ack, which is ypically simple , as e , and equi es ewe dependencies [63]. UNDERSTANDING LiDAR 1 LiDAR 1 Fusion Objec De ec ion Shape Ex ac ion LiDAR 1 LiDAR 1 LiDAR 1 LiDAR N D i e N T ans o m Fil e G ound Fil e ing D i e 1 T ans o m Fil e LiDAR 1 LiDAR N D i e 1 D i e N DATA FLOW SENSING Figu e 3. LiDAR p ocessing s ack. This s ack includes he ollowing s eps in he sensing and unde s anding ope a ions: (1) he so wa e d i e s ansla e aw da a e ie ed om he senso in o s uc u ed 3D poin cloud da a; (2) nex , da a is s a is ically ans o med/ il e ed o emo e possible noise o undesi ed poin s om he poin cloud; (3) da a usion can be used o c ea e a unique ep esen a ion o he ehicle’s su oundings; (4) a g ound il e ing s ep is applied o isola e g ound poin s om non-g ound da a; (5) he objec de ec ion ask iden i ies objec s in he poin cloud da a; and, inally (6) he e is a shape ex ac ion s ep o enable and pe o m he objec classi ica ion. Rega ding he g ound segmen a ion ask, i s basic concep can be ound in Figu e 4, whe e he aw da a collec ed om he LiDAR is p ocessed o Senso s 2023,23, 601 7 o 30 sepa a e he poin s ep esen ing he ehicle’s su ounding en i onmen om he g ound poin s. Acco ding o he subsequen asks, il e ed da a can be used o he ehicle o sa ely na iga e he en i onmen , o o he objec de ec ion and classi ica ion s eps. In his pape , we s udy mos o he ele an con ibu ions ha could be ound in he s a e-o - he-a o he g ound il e ing s ep, esul ing in he axonomy illus a ed in Figu e 5. (a) Raw LiDAR da a. (b) Fil e ed en i onmen and objec s da a. (c) Fil e ed g ound da a. Figu e 4. Basic p inciple o he g ound segmen a ion ask. Figu e 5. Classi ica ion and axonomy o exis ing g ound segmen a ion me hods. Cu en algo i hms can be classi ied in o i e di e en ca ego ies: (1) 2.5D G id- based algo i hms, which can be u he di ided in Occupancy G ids and Ele a ion Maps ( ha u he include Mul i-le el and Mean-based algo i hms); (2) G ound Modelling, which can use Gaussian P ocess Reg ession (GPR), Line Ex ac ion, and Plane Fi ing app oaches; (3) Adjacen poin s and Local ea u es, which include Channel-based, Range Image, Clus e ing, and Region G owing me hods; (4) Highe O de In e ence, which deploy me hods based on Condi ional Random Field (CRF) and Ma ko Random Field (MRF) app oaches; (5) and Lea n-Based algo i hms, which mainly apply CNNs o iden i y and pe o m he g ound segmen a ion asks. Senso s 2023,23, 601 8 o 30 3.1. 2.5D G id-Based Cu en ly, mode n LiDAR senso s may hold housands o poin s in hei 3D poin cloud ep esen a ions, which makes i ha de o analyze he en i e poin cloud in eal ime du ing he na iga ion asks. To help in mi iga ing hese p oblems, g id-based echniques use a essella ed 2D ep esen a ion o he 3D space, whe e each cell con ains in o ma ion abou he poin s inside. The u iliza ion o his echnique can d as ically educe he compu- a ional and memo y equi emen s associa ed wi h he 3D poin cloud ep esen a ion. Ele a ion Maps: This echnique is he mos used in 2.5D g id ep esen a ions. As depic ed by Figu e 6( op iew ep esen a ion o he su ounding en i onmen ), each cell con ains ele an in o ma ion abou all poin s inside. Ele a ion maps can p o ide ad an ages in e ms o noise educ ion when compa ed wi h o he me hods. Howe e , hey s ill ace p oblems in e ms o e ical space ep esen a ion as hey ail o model he emp y space be ween poin s, i.e., o e hangs and ee ops. Ne e heless, due o i s simplici y, se e al algo i hms le e age his echnique o g ound plane segmen a ion. Douilla d e al. [ 64 ] use a mean-based ele a ion map, i.e., each cell con ains he a e age heigh o all poin s, ollowed by clus e ing echniques o de ec and emo e he g ound poin s. The algo i hm ollows h ee simple s eps: (1) calcula es he su ace g adien s o each cell and classi ies p o uding objec s and g ound cells; (2) clus e s adjacen g ound cells; and (3) co ec s a i ac s w ongly gene a ed by he g adien compu a ions, e.g., i a cell ha was classi ied as an objec has an a e age heigh close o i s neighbo ing g ound cells, i is changed o a g ound cell. Despi e p esen ing good pe o mance esul s, he p oposed solu ion s ill needs u he imp o emen s o achie e eal- ime cons ain s. LiDAR 0 Figu e 6. Visual ep esen a ion o an Ele a ion Map. The wo ks p oposed by As adi e al. [ 65 ] and Li e al. [ 66 ] include, on each cell o he 2.5D ele a ion g id, he a e age heigh and he a iance be ween all poin s. I bo h alues a e lowe han a con igu ed h eshold, he cell is conside ed la and classi ied as pa o he g ound plane. The i s me hod was e alua ed wi h he KITTI da ase using poin cloud da a om a Velodyne HDL-64, and he la e wi h da a om a Velodyne HDL-64E senso on an In el i7 dual-co e p ocesso unning a 3.4 GHz wi h 8 GB o RAM. Despi e his me hod allowing o e y accu a e de ec ion o plana objec s, i can w ongly classi y objec s close o he g ound as belonging o he g ound plane. To add ess his issue, Meng e al. [ 67 ] apply a heigh di e ence ke nel o e each cell and i s neighbo s, allowing o de ec ing cells wi h a sligh ly highe a e age heigh han neighbo ing cells. Despi e imp o ing he de ec ion o low-heigh objec s, ex a s eps a e necessa y o add ess une en e ains, which signi ican ly inc eases he o e all algo i hm’s complexi y. The e a e se e al applica ions o hese simple echniques, e.g., Tanaka e al. e alua e e ain’s a e sabili y by calcula ing i s Senso s 2023,23, 601 9 o 30 oughness and slope using an ele a ion map c ea ed om a HOKUYO UTM-30LX LiDAR moun ed on a Pionee 3DX mobile obo [68]. Using ele a ion maps p esen s some disad an ages when ying o model ee e ical space be ween poin s, i.e., in o e hanging objec s, limi ing i s u iliza ion in some ou doo scena ios. Fo ins ance, using mean-based algo i hms in he scena io depic ed in Figu e 7a causes he sys em o classi y he a ea below he ee as e ain unable o be d i en. To mi iga e his, P a e al. [ 69 ] p oposed an algo i hm ha allows mobile obo s o model e ical and o e hanging objec s in ele a ion maps by classi ying he su oundings in o ou dis inc classes: (1) egions sensed om abo e; (2) e ical s uc u es; (3) e ical gaps; and (4) a e sable cells. The g id map esul ing om his me hod is depic ed in Figu e 7b. The algo i hm s a s by calcula ing he heigh a iance o each cell, applying a Euclidean clus e ing echnique nex . The clus e ing o poin s whose dis ance wi hin he cell is smalle han 10 cm allows o he de ec ion o emp y spaces, which is essen ial o iden i ying o e hangs. Nex , when an o e hang is de ec ed, only he lowes a e age heigh clus e is conside ed o calcula e he a e age heigh inside he cell. Despi e co ec ly iden i ying d i - able a eas below o e hangs, his echnique does no allow o he simul aneous modeling o mul iple su aces, i.e., modeling he e ain abo e an unde pass. To sol e his issue, he wo k p oposed by T iebel e al. [70] addi ionally s o es he in e als be ween he de ec ed clus e s o su aces (p e iously, only he lowes was conside ed), allowing o he co ec ep esen a ion o all he o e hanging su aces. Bo h wo ks we e e alua ed on a Pionee II AT obo equipped wi h a SICK LMS291 senso moun ed on an AMTEC w is PW70. Missclassi ied cells (a) Mean-based ele a ion map (side- iew). Missclassi ied cells T a e sable cells (b) Mul i-le el ele a ion map (side iew). Figu e 7. Ele a ion map echniques wi h o e hangs. Occupancy G id Maps: These algo i hms, in oduced in he 1980s by Mo a ec and El es, use ine-g ained g ids o model he occupied and ee space in he su ounding en i on- men [ 71 ]. The main goal o his echnique is o gene a e a consis en me ic map om noisy o incomple e senso da a. This can be achie ed by measu ing a cell mul iple imes, so ha in o ma ion can be in eg a ed using Bayes il e s [ 72 ]. This me hod is conside ed highly obus and easy o implemen , which is essen ial o au onomous d i ing asks [ 73 ]. S anley, he obo c ea ed by he S an o d Uni e si y ha won he De ense Ad anced Resea ch P ojec s Agency (DARPA) challenge in 2005, used occupancy g ids o obs acle de ec ion [ 74 ]. Fi s , he su oundings a e modeled by assuming each cell is in an occupied, ee, o unknown s a e. A cell is conside ed occupied i he e ical dis ance be ween he maximum and minimum heigh o he de ec ed poin s exceeds a dis ance del a. I his e i- ica ion ails, he a ea is conside ed d i able, meaning he g ound is success ully de ec ed. In he ollowing edi ions o he DARPA Challenge, se e al eams also used occupancy g ids o modeling he d i able a ea, which helped hem in inishing he challenge [ 75 – 77 ]. Simila ly, Himmelsbach e al. [ 78 ] p oposed he u iliza ion o occupancy g ids allied wi h ehicle posi ion de ec ion mechanisms o accu a ely segmen he g ound plane o he Senso s 2023,23, 601 16 o 30 (a) Poin cloud ame. (b) Range image. Figu e 14. Con e sion o a poin cloud ame o a ange image ep esen a ion. 3.4. Highe O de In e ence One o he bigges challenges associa ed wi h analyzing LiDAR da a is ha as he dis ance o he senso inc eases, he poin s wi hin he poin cloud become spa se a longe anges. This spa si y can lead o alse classi ica ions in poin cloud segmen a ion me hods, ep esen ing a signi ican d awback in au omo i e applica ions. Highe o de in e ence me hods ha e p o en use ul in mul iple compu e ision asks, e.g., seman ic segmen a ion [ 114 ], which has led o hei ecen implemen a ion in LiDAR sys ems o o e come p oblems ela ed o g ound segmen a ion o spa se poin cloud scena ios. This g oup o me hods includes me hods based on he MRF and CRF algo i hms. Ma ko Random Field (MRF): An MRF model can be seen as an undi ec ed g aph, whe e nodes ep esen andom a iables and edges ep esen desi ed local in luences be ween pai s o nodes. In he case o LiDAR da a, he poin s a e he nodes o he g aph and he edges can be modeled using he heigh alues. Guo e al. [ 115 ] and Byun e al. [ 116 ] sugges he combina ion o an MRF wi h a Belie P opaga ion (BP) algo i hm o iden i y he d i able a ea, e en a high dis ances. None heless, hese app oaches s ill ace de ec ion p oblems when d i ing in ough e ains. The wo k om Zhang e al. [ 117 ] ies o imp o e hese app oaches o be used bo h in ough and une en e ains. I implemen s cos unc ions o gene a e p obabilis ic g ound heigh measu emen s, c ea ing models ha compensa e he loss o in o ma ion due o pa ial obs uc ion om close objec s. Based on his in o ma ion, combined wi h a BP algo i hm, a mul i-label Ma ko ne wo k is used o he g ound segmen a ion ask. The p oposed me hod shows p omising esul s in spa se poin cloud dis ibu ions, achie ing alse posi i e a es as low as 2.12% in complex o - oad en i onmen s. Howe e , he a e age p ocessing ime in he men ioned scena io was abo e 1 s using an In el Co e p ocesso unning a 3.2 GHz, which makes i s u iliza ion ha d in embedded pe cep ion sys ems wi h eal- ime equi emen s. O he wo ks use simila app oaches combined wi h heigh his og ams o es ima e he g ound heigh ange, ollowed by he MRF model o e ine he labeling o g ound poin s [118]. Senso s 2023,23, 601 17 o 30 Huang e al. [ 119 ] p opose an algo i hm ha aims a sol ing he high compu a ional equi emen s o MRF-based me hods. This algo i hm s a s by pe o ming a coa se seg- men a ion based on a ing-based ele a ion map whe e da a poin s a e a anged in ings whose diame e s a e p opo ional o he dis ance o he LiDAR senso . Since his ype o al- go i hm assumes ha he g ound in he g id is la , which is no he case in mos eal-wo ld scena ios, an op imiza ion algo i hm based on spa io empo al adjacen poin s is applied. The ea e , o pe o m a mo e e ined segmen a ion, he poin cloud is con e ed in o a ange image whe e each poin is p ojec ed as a g aph node o c ea e an MRF model, unlike he p e ious app oaches ha con e g ids in o g aph nodes [ 94 , 114 ]. Ne e heless, MRF me hods based on i e a i e implemen a ions, such as he BP algo i hm, a e usually compu- a ionally expensi e and ime-consuming o pe o m he segmen a ion ask. To mi iga e his, his app oach ini ializes he ne wo k wi h in o ma ion ega ding he high-con idence obs acle poin s and g ound poin s cap u ed om he segmen a ion algo i hm, which helps in educing he compu a ional complexi y and a oids con e gence p oblems ha usually exis in o he implemen a ions. Finally, he algo i hm applies he g aph cu segmen a ion echnique, depic ed in Figu e 15, o sol e he model, which helps in achie ing he desi ed ine segmen a ion esul s. Due o he educ ion in he algo i hm’s complexi y, he p oposed wo k can achie e an a e age p ocessing ime o 39.77 ms wi h a single co e om an In el i7-3770 p ocesso while using he KITTI da ase con aining da a om a Velodyne HDL-64E. Cu O iginal Segmen ed Figu e 15. G aph Cu segmen a ion me hod. Condi ional Random Field (CRF): CRF is a subse o MRF ha labels sequences o nodes gi en a speci ic chain o obse a ions, which imp o es he abili y o cap u ing long- ange dependencies be ween hem. Rummelha d e al. [ 120 ] p opose he addi ion o spa ial and empo al dependencies o CRF o model he g ound su ace. Thei me hod di ides he en i onmen in o di e en in e connec ed ele a ion cells, which is in luenced by local obse a ions and spa io- empo al ela ionships. The empo al cons ain s a e inco po a ed in o he segmen a ion da a using a dynamic Bayesian amewo k, allowing o a mo e accu a e modeling o g ound poin s. The p oposed me hod was i s es ed on an In el Xeon W3520 p ocesso unning a 2.6 GHz wi h 8 GB o RAM and wi h a Quad o 2000 g aphics ca d wi h 2 GB o ideo memo y, achie ing a ame p ocessing equency o 6.8 Hz wi h da a om a Velodyne HDL-64E senso . Howe e , he au ho s claim ha wi h expe imen al pla o ms such as Teg a X1 and K1, he algo i hm achie ed eal- ime pe o mance. Simila ly, Senso s 2023,23, 601 18 o 30 and ollowing he app oach o using CRF me hods in he segmen a ion o da a om digi al came as [ 121 ], Wang e al. [ 122 ] model g ound da a by ep esen ing he CRF in a 2D la ice plane. Nex , i uses he RANSAC on he c ea ed plane o ex ac he g ound poin s. Despi e p esen ing good segmen a ion esul s, i equi es se e al i e a ions o co ec ly ex ac he poin s in une en e ains, which hea ily comp omises eal- ime pe o mance. Addi ionally, hese me hods a e e y compu a ionally-hung y, which makes hem unsui able o eal- ime scena ios unless specialized ha dwa e accele a ion is used. 3.5. Lea n-Based Lea n-based me hods ha e been widely applied o came a ision sys ems, achie ing good esul s in a a ie y o segmen a ion asks, including g ound segmen a ion. None he- less, due o he inhe en bene i s o deploying LiDAR senso s in he pe cep ion sys em o he ca , se e al lea n-based me hods applied o poin cloud da a began o eme ge [ 123 ]. Back in 1988, Pome leau [ 124 ] in oduced Au onomous Land Vehicle In a Neu al Ne wo k (ALVINN), a 3-laye back-p opaga ion ne wo k designed o he ask o oad ollowing ha was ained om mul iple simula ed oad images. Despi e he solu ion no allowing he ehicle o ollow he oad a high speeds, p ima ily due o compu a ional limi a ions a he ime, i was good enough o alida e his new app oach and o demons a e ha lea ning-based me hods could become po en ial solu ions o au onomous d i ing asks. The success o ALVINN has igge ed he u iliza ion o machine lea ning algo i hms ap- plied o LiDAR da a, which, based on he success o hese app oaches, se e al algo i hms ha e eme ged and many mo e a e cu en ly being eleased. Thus, i makes i e y ha d o compile all he exis ing in o ma ion in his sec ion. None heless, we p o ide he mos signi ican algo i hms ha made g ea con ibu ions o his opic. Poin Ne [ 125 ] is a uni ied a chi ec u e o di e en applica ions, e.g., objec classi i- ca ion and segmen a ion, scene seman ic pa sing, e c. Despi e showing p omising esul s esis an o inpu pe u ba ion and co up ion, by design, his a chi ec u e is only sui - able o small-scale poin clouds. This is mainly due o he weak lea ning capabili ies o local ea u es, a ec ing i s pe o mance in la ge-scale complex poin clouds and limi ing i s capaci y o iden i y ine-g ained pa e ns. Aiming a imp o ing he p e ious wo k, Poin Ne ++ [ 126 ] implemen s a hie a chical amewo k o disco e he mul i-scale spa ial con ex ual elemen s, which imp o es pe o mance in la ge-scale poin clouds. None heless, he hie a chical s uc u e inc eases he o e all compu ing complexi y. O he eme ging app oaches, such as he Poin wise CNN [127], ocus on poin wise con olu ion ope a ions applied o each poin in a poin cloud, which achie es good accu acy esul s in he seman ic segmen a ion and objec ecogni ion asks. SPG aph [ 128 ] o ganizes he poin cloud using a da a s uc u e known as he Supe Poin G aph (SPG), which is c ea ed om pa i ioning he scanned image in o geome ically homogeneous elemen s. The e alua ion was con- duc ed on a 4 GHz CPU and GTX 1080 Ti GPU o e wo di e en da ase s, Seman ic3D and he d S an o d La ge-Scale 3D Indoo Spaces (S3DIS). Py amidPoin [ 129 ] employs a dense py amid s uc u e ha p o ides a second glance a he poin cloud, allowing he ne wo k o e isi dis inc laye s o med om o he ne wo k le els, which enables ea u e p opaga ion. Howe e , hese me hods equi e expensi e nea es -neighbo sea ch algo i hms in spa se space and a e ine icien when dealing wi h ou doo and au omo i e poin clouds. None heless, based on hei success, hese p inciples ha e been used o help sol e he g ound segmen a ion p oblem. The expe imen s we e pe o med wi h h ee di e en ypes o da ase s, i.e., ae ial, e es ial, and mobile lase scanne s, on a single NVIDIA TITAN RT GPU. Paigwa e al. [ 130 ] de eloped he GndNe , whose s eps a e shown in Figu e 16. The 3D poin cloud g ound plane ele a ion is es ima ed in a g id-based ep esen a ion, and he poin s a e di ided in o g ound and non-g ound ca ego ies. The me hod s a s by ans o ming he poin cloud in o an e enly spaced 2D g id, p oducing a se ies o poin -popula ed pilla s. Then, o ex ac he ea u es con ained in each non-emp y pilla , a simpli ied e sion o he Poin Ne me hod is used [ 125 , 126 ], which gene a es a pseudo- Senso s 2023,23, 601 19 o 30 image o he g id. Then, a 2D con olu ional encode -decode ne wo k p ocesses his image o p oduce a high-le el ep esen a ion. Howe e , o aining he ne wo k, i is necessa y o use a labeled g ound ele a ion map da ase . To sol e his, he au ho s used he Seman icKITTI [ 81 , 131 ] and modeled he g ound plane wi h a CRF me hod o gene a e a g ound- u h ele a ion map sui able o he equi ed aining. This allowed GndNe o achie e a mean in e sec ion o e union (mIoU) alue o 83.6%, while p o iding eal- ime pe o mance wi h an a e age p ocessing ime o 17.98 ms pe ame. Raw 3D Poin Cloud Segmen a ion Resul GndNe Me hod Poin Cloud Disc e iza ion Fea u e Ex ac ion Con olu ional Encode /Decode Ne wo k P ocessing Th eshold Based Segmen a ion 2D Pilla G id 2D Pseudo Image G ound Ele a ion Es ima ion Figu e 16. O e iew o he GndNe algo i hm. Despi e he ma u i y o poin -wise app oaches, new app oaches s a by ep esen ing poin clouds as s uc u es be o e applying he lea ning-based algo i hms, which helps in achie ing mo e e iciency while equi ing less compu a ional esou ces [ 132 ]. Fo in- s ance, VoxelNe [ 133 ], disc e izes poin clouds in o oxels be o e using a 3D CNN. The p oposed Voxel Fea u e Encoding (VFE) laye in eg a es ea u e ex ac ion and bounding box p edic ion in o a single s age, allowing he algo i hm o achie e an in e ence ime o a ound 33 ms and a p ecision o up o 89.6% on a p ocessing sys em composed o a Ti anX GPU wi h a CPU unning a 1.7 GHz. On he o he hand, SPLATNe [ 134 ] in e pola es poin clouds in o pe mu ohed al spa se la ices be o e execu ing a 3D CNN, enabling a s aigh o wa d mapping o 2D in o ma ion in o 3D and ice- e sa. Cylinde 3D [ 135 ] builds a cylind ical pa i ioning and hen uses an asymme ical 3D con olu ion ne wo k ha enables i o each an mIoU o 61.8% using he Seman icKITTI da ase , which p o ides da a om a Velodyne HDL-64E LiDAR senso . Poin Pilla [ 136 ] is a no el encode me hod ha uses Poin Ne [ 125 , 126 ] o c ea e pilla ep esen a ions om he inpu poin clouds. This ep esen a ion can hen be used by a 2D con olu ional a chi ec u e o p ocess i in o highe -le el ep esen a ions o he iden i ica ion s eps. Addi ionally, his me hod can achie e eal- ime pe o mance, wi h in e ence aking 16.2 ms pe ame on a compu ing sys em composed o an In el i7 p ocesso and a 1080 i GPU. SqueezeSeg [ 137 – 139 ] is ano he wo k ha uses hese pa icula s uc u es, whe e a 2D sphe ical p ojec ion poin -wise label map is used and subsequen ly e ined by a CRF implemen ed as a ecu en laye . I s mos ecen e sion, SqueezeSegV3, suppo s wo di e en implemen a ions ha can achie e di e en pe o mance esul s. SSGV3-53 eaches an mIoU o 52.2% wi h 11 ames pe second and SSGV3-21 achie es an mIoU o 48.8% wi h 16 ames pe second. RangeNe ++ [ 140 ] is a deep-lea ning me hod ha uses ange images and 2D con olu ions, ollowed by GPU-accele a ed pos -p ocessing me hods, o eco e consis en seman ic in o ma ion o comple e LiDAR scans. Simila ly o SqueezeSeg, RangeNe p esen s wo implemen a ion e sions, whe e RangeNe 21 can achie e an mIoU o 47.4% wi h 20 ames pe second and RangeNe 53 an mIoU o 49.9% wi h 13 ames pe second. All es s we e conduc ed on GPU-based se ups wi h he KITTI da ase . Finally, Pola Ne [ 141 ] uses a pola Bi d’s-Eye-View (BEV) ep esen a ion, which balances he poin s ac oss g id cells in a pola coo dina e sys em, allowing i o achie e an mIoU o 54.3% wi h 16.2 ames pe second on he Seman icKITTI da ase . Se e al wo ks use CNNs o iden i y and segmen obs acles wi hin a LiDAR poin cloud. Howe e , pe o ming lea n-based algo i hms on LiDAR da a equi es a signi ican amoun o p ocessing powe which o en ansla es in o e y ime-consuming asks. Consequen ly, Senso s 2023,23, 601 20 o 30 Lyu e al. [ 142 ] p oposed an e icien FPGA-based solu ion ha can p ocess each LiDAR scan wi hin 16.9 milliseconds using he KITTI da ase [ 81 , 131 ] as a es bed. Velas e al. [ 143 ] used CNNs o pe o m g ound segmen a ion, conside ably educing he algo i hm’s p ocessing ime when compa ed wi h p e ious lea n-based app oaches, bu ailing o achie e eal- ime pe o mance in a CPU implemen a ion (In el i5-6500 p ocesso ). The bes pe o mance esul s we e achie ed wi h GPU accele a ion (NVIDIA GeFo ce GTX 770), whe e di e en ne wo k opologies we e es ed. Wi h a opology consis ing o 5 con olu ional laye s plus a single decon olu ion, he achie ed p ocessing ime o one Velodyne HDL-64E ame is, on a e age, less han 7 ms. Ne e heless, his me hod can only be applied o g ound poin segmen a ion a he han o he segmen a ion asks. Zhang e al. [ 144 ] in oduced ShellNe , a me hod o applying deep lea ning o 3D poin clouds. I is buil on a con olu ion ope a o ha ep esen s poin se s wi h locally c ea ed sphe ical shells. This me hod can esul in as poin cloud segmen a ion and ou pe o m o he me hods in e ms o accu acy. Howe e , he lack o labeled aining da a s ill poses a signi ican obs acle o he algo i hm’s pe o mance in many scena ios. Shen e al. [ 145 ] p opose a me hod based on he jump-con olu ion-p ocess (JCP) o sol e he p oblems aced by segmen a ion algo i hms in handling complica ed e ains, and he excessi e p ocessing ime and memo y equi emen s. The me hod s a s by p ojec ing on o an RGB image he poin cloud p e iously labeled by an imp o ed local ea u e ex ac ion algo i hm. The pixel alue is hen ini ialized wi h he poin ’s label and con inuously upda ed using image con olu ion. Finally, i is used he con olu ion p ocess wi h a jump ope a ion o pe o m ope a ions only on he low-con idence poin s il e ed by he c edibili y p opaga ion mechanism, which educes he execu ion ime. This me hod shows good pe o mance esul s in di e en en i onmen s, achie ing an a e age p ocessing ime o 8.61 ms and 15.62 ms when dealing wi h 64-beam and 128-beam LiDAR da a, espec i ely. He e al. [ 132 ] p opose Sec o GSne , an end- o-end DNN amewo k designed o pe o m g ound segmen a ion o ou doo LiDAR da a. The algo i hm s a s wi h a sec o encode module in which he 3D poin cloud is di ided in o di e en sec ions based on a BEV sec o pa i ioning me hod, as depic ed in Figu e 17. The poin cloud is ep esen ed in a ci cula egion, ollowed by i s di ision in o equal slices (W) and adial line segmen s (H) ha a e dynamically adjus ed along he adius so ha he poin s become mo e e enly dis ibu ed ac oss he g id. W W H z y x Spa se Figu e 17. BEV sec o pa i ion ep esen a ion. A e pa i ioning he poin cloud, some ea u es, e.g., poin coo dina es and in ensi y, a e agg ega ed in each sec o using a simpli ied e sion o he Poin Ne [ 125 , 126 ] encode , esul ing in he gene a ion o a sec o ea u e map. Then, he sec o encode module Senso s 2023,23, 601 21 o 30 ecei es he sec o ea u e map o p ocess i using he UNe CNN o p edic he sec o labels. In he inal s age, a sec o - o-poin es o a ion is pe o med o econs uc he segmen ed poin cloud. All models we e ained on a desk op compu e wi h an NVIDIA GTX2080Ti g aphics ca d and an In el i7 6700k p ocesso , esul ing in in e ence esul s a 170.6 Hz. 4. Discussion When choosing a g ound segmen a ion algo i hm, i is c ucial o unde s and hei mos signi ican di e ences and ea u es ha bes sui he equi emen s o he inal appli- ca ion. Table 1summa izes a quali a i e compa ison o cu en s a e-o - he-a g ound segmen a ion me hods ega ding he ollowing me ics: eal- ime ea u es, compu a ional equi emen s (which a e some imes dic a ed by he algo i hm’s complexi y o s eps o achie e eal- ime capabili ies), he segmen a ion immuni y, and he abili y o deal wi h ising obs acles and egions, une en g ound, and spa se da a in he poin cloud. This com- pa ison is made solely based on a ailable in o ma ion e ie ed om espec i e li e a u e, whose expe imen al se ups a e o he esponsibili y o espec i e au ho s. Table 1. Quali a i e compa ison be ween exis ing g ound segmen a ion me hods. Me hod Me ic Real-Time Compu a ional Requi emen s Segmen a ion Immuni y Pe o mance wi h Rising Obs acles and Regions Pe o mance wi h Une en G ound Pe o mance wi h Spa se Da a 2.5D G id-based Mean-Based [64–68]3[66,68]; 7[64] Low Unde -/o e -segmen a ion Good [67,68] Good [67,68] - Mul i-le el [69,70]- Medium - Good Good - Occupancy G id [74–79]3[74–78]; 7[79] High Unde -segmen a ion [74] Good [79] - - G ound Modelling GPR-based [90–93]3[91]; 7[90,92,93]High Unde -segmen a ion [91]Insensi i e o slowly ising obs acles [91]Good [91,93] Good [90,92,93] Line Ex ac ion [87,88]3[87] Medium Sligh O e -/unde -segmen a ion Good [88] Good [88]; Bad [87] - Plane Fi ing [80,82–86]3[82–86]; 7[80] Medium/High P one o o e -segmen a ion [82]; Unde -segmen a ion [83] Good [83,86]Good [83,86]; Bad [80]Good [82] Adjacen Poin s and Local Fea u es Channel-based [94–100]3[95–98,100]; 7[94]Medium Unde /o e -segmen a ion [ 94 ]; Unde /o e -segmen a ion [97]Good [94–98] Good [94,97] Good [94] Range Images [109–112]3[109,110,112]; 7[111]Medium P one o o e -/unde -segmen a ion; O e -segmen a ion [109,110] Good [109,110] - Good [109,110] Clus e ing [ 90 , 105 , 106 ] 7[90] Medium/High Unde -/o e - segmen a ion [105] Good [106] - Good [90] Region G owing [101–104]7[101] Medium/High Small o e -segmen a ion [101,102,104]; Small unde - /o e -segmen a ion [103] Good [104] - - Highe O de In e ence MRF [115–119]3[119]; 7[117] High - Good [115,116] Good [115,117]; Bad [116] Good [115,116] CRF [120,122]3[120](wi h GPU); 7[122]High - - - Good [120] Deep Lea ning CNN [ 125 – 130 , 132 – 144 ] 3[130,133,136,140] (wi h GPU), and FPGA [142]; 7[143] High/Ve y High - Good Good Good Real- ime: Wi hin an au onomous d i ing scena io, i is manda o y o he pe cep ion sys em o p ocess and unde s and he su ounding en i onmen in eal ime, which means ha , o a gi en senso o a se o senso s, he s eps o he LiDAR p ocessing s ack mus be pe o med wi hin a known pe iod o ime so ha he d i ing decisions can be aken wi hin a sa e ime ame. A high- esolu ion LiDAR senso can gene a e millions o da a poin s. Fo ins ance, he Velodyne VLS-128 can p oduce up o 9.6 M poin s pe second in he dual- e u n mode, wi h ame a es a ying om 5 Hz o 10 Hz. In a ypical ope a ion, his senso can be con igu ed o p oduce, on a e age, a poin cloud o 2,403,840 poin s Senso s 2023,23, 601 22 o 30 pe second (240,384 poin s a 10 Hz), which means ha such an amoun o da a mus be p ocessed in unde 100 ms ac oss all so wa e s ack laye s. Rega ding he g ound segmen a ion asks, wi h a ew excep ions, bo h 2.5D G id-based and G ound Modelling me hods can achie e eal- ime p ocessing. Fo he algo i hms based on he mul i-le el app oach [ 69 , 70 ], his in o ma ion could no be e ie ed. Due o hei complexi y, mos o he GPR-based me hods [ 90 , 92 , 93 ] and one Plane- i ing me hod [ 80 ] a e unable o p o ide he desi ed eal- ime ea u es. Conce ning he algo i hms based on adjacen poin s and local ea u e ex ac ion, i.e., Region G owing [ 101 ], Clus e ing [ 90 ], one om Range Images [ 111 ], and one om Channel-based [ 94 ], hey do no p o ide eal- ime (likely due o lack o op imiza ions o ha dwa e se up used), while he emaining me hods [ 94 – 98 , 100 , 109 , 110 , 112 ], a e consid- e ed eal- ime. Rega ding he Highe O de In e ence [ 115 – 120 , 122 ] and Deep lea ning [ 125 – 130 , 132 – 144 ] me hods, hey na u ally do no p o ide eal- ime g ound segmen a ion due o he o e all algo i hm’s complexi y. Howe e , when eso ing o ha dwa e accel- e a ion, e.g., based on GPUs [ 120 , 130 , 133 , 136 , 140 ] o FPGA [ 142 ], o aking ad an age o coa se segmen a ion esul s om local ea u e ex ac ion [ 29 ], some solu ions can mee eal- ime equi emen s. Compu a ional Requi emen s: When deployed in au omo i e applica ions, he compu a- ional equi emen s associa ed wi h g ound segmen a ion me hods a e a c ucial me ic o conside , mainly because he pe cep ion sys em is o en composed o embedded p o- cessing en i onmen s ha y o minimize he a ailable ha dwa e esou ces. Me hods based on Ele a ion Maps p esen he lowes compu a ional equi emen s as hey analyze a 2D essella ed ep esen a ion o he su oundings, ins ead o he en i e 3D ep esen a ion. Rega ding he o he 2.5D g id-based app oaches, Mul i-le el algo i hms equi e ex a classi ica ion s eps, while Occupancy G ids equi e he in e pola ion o da a and he use o Bayes il e s, which inhe en ly inc eases he memo y and compu a ional needs in bo h cases. Likewise, GPR-based and Plane Fi ing me hods (based on RANSAC) ely on i e a- i e app oaches, consequen ly inc easing he memo y equi emen s. Addi ionally, GPR me hods equi e complex calcula ions o p ocess he poin cloud, which u he inc eases he compu a ional needs. On he o he hand, Line Ex ac ion app oaches ea u e a lowe esou ce consump ion han he emaining G ound Modelling me hods since hey di ide he poin cloud in o a pola g id map, simpli ying he equi ed compu a ions. Conce n- ing he Adjacen Poin s and Local Fea u es me hods, Channel-based and Range Image app oaches a e no conside ed e y compu a ionally in ensi e. Howe e , hey a e based on he analysis o geome ic condi ions be ween poin s, which can ep esen he need o specialized ha dwa e, such as a loa ing-poin uni o igonome ic calcula ions. On he o he hand, despi e hei simplici y, Clus e ing and Region-G owing me hods equi e e y i e a i e ope a ions, which can ep esen high memo y equi emen s especially o la ge poin clouds wi h poin s con aining mul iple ea u es, e.g., a ge ’s e lec i i y. On he o he side, Highe O de In e ence me hods ea u e high compu a ional equi emen s due o he associa ed complex ma hema ical compu a ions and espec i e i e a i e s eps, e.g., he BP algo i hm, which ansla es in o high memo y equi emen s. Finally, among all me hods, Lea n-based app oaches demand he highes compu a ional needs. This is mainly caused by he ex ensi e complex compu a ions, signi ican memo y u iliza ion, and some imes specialized ha dwa e, e.g., GPUs, gene ally associa ed wi h CNN implemen a ions. Segmen a ion Immuni y: Segmen a ion immuni y e e s o he algo i hm’s suscep ibili y o unde - and/o o e -segmen a ion, which co esponds o ei he oo coa se o oo ine segmen a ion, espec i ely. When he unde -segmen a ion occu s, he poin s belonging o di e en objec s a e me ged in o he same g ound segmen . On he o he hand, wi h o e - segmen a ion, a single objec can be ep esen ed by se e al clus e s. In many applica ions, unde -segmen a ion is conside ed a mo e se e e issue han o e -segmen a ion, since he w ong classi ica ion o he g ound plane can lead o sa e y issues o he au onomous ehicle. 2.5D G id-based Ele a ion Map algo i hms end o su e bo h om unde - o o e - Senso s 2023,23, 601 23 o 30 segmen a ion, which can highly a ec he o e all algo i hm’s pe o mance and accu acy. Tha happens especially when he g ound is signi ican ly sloped o cu bed. G ound Modelling’s Line Ex ac ion and Plane Fi ing [ 82 ] me hods end o sligh ly su e om unde - o o e -segmen a ion. Howe e , GPR-based [ 91 ] and Plane Fi ing [ 83 ] a e able o o e come unde -segmen a ion issues. Adjacen Poin s and Local Fea u es algo i hms ypically a e suscep ible o unde - o o e -segmen a ion, excep o [ 103 ], which is immune o o e -segmen a ion, while [97,105] a e immune o bo h unde - and o e -segmen a ion. Pe o mance wi h Rising Obs acles and Regions, Une en G ound, and Spa se Da a: In a eal-wo ld d i ing scena io, he g ound is no la and, ising obs acles, egions, and une en g ound usually ep esen a signi ican challenge o g ound segmen a ion algo i hms. Addi ionally, dealing wi h spa se poin clouds can lead o pe o mance loss o algo i hm inabili y o sol e he segmen a ion ask. The e o e, o assess he e sa ili y and sa e y o a me hod in di e en si ua ions, i is c ucial o e alua e he segmen a ion pe o mance wi h ising obs acles, slopped o ough e ains, and spa se da a. Rega ding 2.5 G id- based me hods, since he g ound is modeled in o a g id whe e each cell ep esen s a small egion o he g ound plane, almos all o hem can pe o m well wi h ising obs acles and une en g ound su aces [ 67 – 70 ]. Howe e , hey can be unp edic able when dealing wi h spa se da a. The G ound Modeling app oaches based on GPR algo i hms can su e om insensi i i y o hese slowly ising obs acles [ 91 ].Since he g ound di ision in independen angula sec o s does no gua an ee he gene al g ound ele a ion con inui y, some obs acles, such as s ai s eps, may be classi ied as g ound. None heless, he o he app oaches, i.e., Line Ex ac ion [ 88 ], and Plane Fi ing [ 83 , 86 ], can handle hese objec s p ope ly. Conce ning he une en g ound egions, some G ound Modelling app oaches canno handle hem p ope ly [ 80 , 87 ], e.g., he Plane Fi ing me hod [ 80 ] uses RANSAC in o de o es ima e he g ound plane. The e o e, he assump ion o a single g ound plane leads o alse classi ica ions and complex da a can deg ade he RANSAC pe o mance. None heless, mos GPR-based algo i hms [ 90 , 92 , 93 ], and one Plane Fi ing me hod [ 82 ], can achie e good segmen a ion esul s when handling spa se da a. F om he Adjacen Poin s and Local Fea u es me hods, mos o hem can handle ising obs acles and egions, e.g., Channel-based [ 94 – 98 ], Range Images [ 109 , 110 ], Clus e ing [106] , and egion G owing [ 104 ]. Rega ding handling une en g ound, some Channel-based app oaches [ 94 , 97 ] can pe o m well in sloped en i onmen s, whe e [ 94 ] de eloped algo- i hms specialized in handling sloped e ains and spa se da a, and [ 97 ] p o ed o achie e good esul s wi h sloped and la en i onmen s. Some me hods [ 90 , 94 , 109 , 110 ] can also pe o m well in spa se da a, highligh ing he wo ks o [ 94 , 109 , 110 ] ha de eloped me h- ods especially o spa se poin clouds. Some Highe O de In e ence MRF me hods can pe o m well in ising obs acles and egions [ 115 , 116 ], as he e a e also o he s ha manage o ob ain good esul s on bo h sloped and ough e ains [ 115 , 117 ]. Un o una ely, no o he in o ma ion could be ound on he o he me ics o he CRF algo i hms. While handling wi h spa se poin clouds, some MRF me hods [ 115 , 117 ] and one CRF me hod [ 120 ] can wo k adequa ely wi h spa se poin clouds. Deep Lea ning me hods gene ally ob ain he mos accu a e esul s in e ms o g ound segmen a ion on well- ained en i onmen s, be- ing able o pe o m well in sloped and ough e ain, as well as in dealing wi h spa se da a. Howe e , he lack o labeled da ase s o he aining phases, he ime-consuming con e sions be ween 3D poin cloud da a and ne wo k inpu da a, he need o powe ul ha dwa e suppo , and he complexi y in ol ed in de eloping algo i hms, usually hampe s he de elopmen o CNNs o he segmen a ion g ound asks. Fu u e ends o g ound segmen a ion me hods: G ound and objec segmen a ion is an impo an ask in au onomous d i ing applica ions, whe e he algo i hm’s pe o mance and he eal- ime aspec s a e he mos c i ical equi emen s when building he pe cep ion sys em o he ehicle. Au omo i e LiDAR senso s a e also becoming mains eam, hus, se e al algo i hms and app oaches o p ocess poin cloud da a a e cons an ly eme ging. Wi h he success o lea n-based app oaches in e ms o accu acy and pe o mance, i is Senso s 2023,23, 601 24 o 30 expec ed ha u u e solu ions will adop CNNs o pe o m he g ound segmen a ion asks [ 123 ]. Wi h he cons an de elopmen o echnology and esea ch a ound hese opics, cu en ly challenges o CNNs wi h LiDAR da a applied o au omo i e, e.g., he lack o da ase s [ 146 ], he high ime-consuming aining phases, he equi emen o powe ul compu ing sys ems, and he algo i hm’s complexi y, a e slowly being mi iga ed. Despi e pe cep ion sys ems being including mo e senso s and LiDAR de ices p o iding mo e esolu ion da a a highe ame a es (which inhe en ly inc eases he amoun o da a o be p ocessed), i is expec ed ha in he nea u u e lea n-based solu ions wi h lowe ha dwa e equi emen s will pe o m he g ound segmen a ion s eps wi h educed p ocessing imes. 5. Conclusions An au onomous ehicle equi es a good pe cep ion sys em o success ully na iga e he en i onmen , equi ed o objec iden i ica ion and obs acle a oidance, and he g ound plane and oad limi s de ec ion asks. De ec ing and classi ying he d i able a ea is undoub - edly a c ucial ask ha equi es ex ac ing in o ma ion om he poin cloud o p ecisely de ec common oad bounda ies. Howe e , his is only possible wi h as and e icien g ound segmen a ion me hods ha can deli e eal- ime ea u es, and choosing he bes ha sui s he inal applica ion will su ely a ec he way he ehicle mo es a ound. This a icle p esen s a ligh weigh su ey o exis ing me hods o de ec and ex ac g ound poin s om LiDAR poin clouds. I summa izes he ex ensi e li e a u e and p oposes a comp ehensi e axonomy o help unde s and he cu en g ound segmen a ion me hods ha can be used in au omo i e LiDAR senso s. The p oposed ca ego ies a e as ollows: (1) 2.5D G id-based me hods, (2) G ound Modeling, (3) Adjacen Poin s and Local Fea u es, (4) Highe O de In e ence, and (5) Lea n-Based me hods, whe e mo e solu ions a e likely o exponen ially eme ge in he e y nea u u e. Mo eo e , and o unde s and he main di e ences be ween hem, his a icle also includes a quali a i e compa ison whe e im- po an me ics, such as eal- ime, compu a ional equi emen s, segmen a ion immuni y, algo i hm’s pe o mance in di e en condi ions, a e discussed. Au ho Con ibu ions: Concep ualiza ion, T.G. and D.M.; me hodology, T.G. and D.M.; alida- ion, T.G., D.M., A.C., L.C. and R.R.; in es iga ion, T.G., D.M., A.C. and L.C.; esou ces, T.G.; w i ing—o iginal d a p epa a ion, T.G., D.M. and R.R.; w i ing— e iew and edi ing, T.G., D.M., A.C. and L.C.; supe ision, T.G.; p ojec adminis a ion, T.G.; unding acquisi ion, T.G.; All au ho s ha e ead and ag eed o he published e sion o he manusc ip . Funding: This wo k has been suppo ed by FCT— Fundação pa a a Ciência e Tecnologia wi hin he R&D Uni s P ojec Scope UIDB/00319/2020 and G an 2021.06782.BD. Con lic s o In e es : The au ho s decla e no con lic o in e es . Re e ences 1. Li man, T. Au onomous Vehicle Implemen a ion P edic ions; Vic o ia T anspo Policy Ins i u e: Vic o ia, BC, Canada, 2021. 2. Socie y o Au omo i e Enginee s (SAE). Taxonomy and De ini ions o Te ms Rela ed o D i ing Au oma ion Sys ems o On-Road Mo o Vehicles (Su ace Vehicle Recommended P ac ice: Supe seding J3016 Jun 2018); SAE In e na ional: Wa endale, PA, USA, 2021. 3. Me cedes-Benz G oup. Fi s In e na ionally Valid Sys em App o al o Condi ionally Au oma ed D i ing. Me cedes 2021 . 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