Robot localization under perceptual aliasing conditions based on laser reflectivity using particle filter

Transcription

1 Robo localizaion under percepual aliasing condiions based on laser refleciviy using paricle filer DongXiang Zhang, Ryo Kurazume, Yumi Iwashia, Tsuomu Hasegawa Absrac Global localizaion, which deermines an accurae global posiion wihou prior knowledge, is a fundamenal requiremen for a mobile robo. Map-based global localizaion gives a precise posiion by comparing a provided geomeric map and curren sensory daa. Alhough 3D range daa is preferable for 6D global localizaion in erms of accuracy and reliabiliy, comparison wih large 3D daa is quie ime-consuming. On he oher hand, appearance-based global localizaion, which deermines he global posiion by comparing a capured image wih recorded ones, is simple and suiable for real-ime processing. However, his echnique does no work in he dark or in an environmen in which he lighing condiions change remarkably. We have proposed a wo-sep sraegy so far, which combines map-based global localizaion and appearance-based global localizaion. Insead of camera images, which are used for appearance-based global localizaion, we use reflecance images, which are capured by a laser range finder as a byproduc of range sensing. However, since his mehod relies on he similariy beween newly-capured and sored reflecance images, he performance is deerioraed in a scene wih high percepual aliasing conaining few or similar feaures such as a corridor and a mine. To deal wih his problem, in his paper, we propose a new global localizaion echnique which combines he proposed wo-sep sraegy and he paricle filer. The effeciveness of he proposed echnique is demonsraed hrough experimens in real environmens. I. INTRODUCTION In numerous pracical applicaions, he exernal environmen around a robo is unpredicable, unsrucured, and unconrolled. For example, in an area ha has been sruck by a srong earhquake or by a mine disaser, previously navigable areas may be blocked by heaps of rubble or collapsed walls, and he geomeric srucure will differ from he original srucure. In order for a robo o efficienly accomplish search and rescue asks in his ype of unknown and unpredicable environmen, accurae map creaion and global localizaion are fundamenal requiremens. A number of global localizaion echniques have been proposed [11]. Appearance-based global localizaion is simple and suiable for real-ime processing. Many camera images are recorded under naural ligh or indoor illuminaion in he environmen, and global localizaion is performed by finding he bes mach using a newly-capured image and sored images[13],[14]. However, appearance-based global posiioning encouners a criical problem in dark environ- Ryo Kurazume, Yumi Iwashia and Tsuomu Hasegawa are wih Faculies of he Graduae School of Informaion Science and Elecrical Engineering, Kyushu Universiy, Japan DongXiang Zhang is wih Phd candidae of he Graduae School of Informaion Science and Elecrical Engineering, Kyushu Universiy, Japan, zhang@irvs.is.kyushu-u.ac.jp mens or in environmens in which he lighing condiion changes dramaically. On he conrary, map-based global localizaion, which deermines he bes posiion a which he observed geomeric feaures from sensory daa mach hose in he provided geomeric map, is a sandard and reliable echnique since his mehod is applicable for various sensors and environmens[1],[8]. From he poin of view of accuracy, comparison of 3D range daa capured by a range sensor and a pre-consruced 3D map is preferable because his will enable precise 6D(posiion and aiude) global localizaion. However, he comparison wih large 3D daa is quie imeconsuming. We have proposed a wo-sep sraegy ha combines map-based global localizaion and appearance-based global localizaion and verified is efficiency in oudoor environmen in [21]. Insead of camera images, which are used for he appearance-based global localizaion, we used reflecance images, which are capured by a laser range finder as a byproduc of range sensing. As a resul of he characerisics of he reflecance image, which is no subjec o significan variaion of he exernal illuminaion condiions, he proposed echnique is useful even in he dark or in an environmen under severe lighing condiions. Furhermore, fas and precise localizaion can be performed by comparing a few 3D range images, which are seleced based on he similariy of he reflecance images. However, since his mehod relies on he similariy beween newly-capured and sored reflecance images, he performance is deerioraed under percepual aliasing condiions such as in a corridor or in a mine. Paricle filer localizaion is an effecive mehod for roboic applicaion by Bayesian inference. The robusness for robo localizaion and mapping in a scene wih few or similar feaures such as a corridor[16],[19], a mine[7] and so on has been demonsraed. Therefore, we propose a new global localizaion echnique which combines he proposed wosep sraegy and he paricle filer. We will demonsrae ha our mehod can achieve robus and precise robo localizaion in environmens wih srong percepual aliasing even under severe variaion of exernal illuminaion condiions. The remainder of he presen sudy is organized as follows. Afer a brief inroducion of relaed research in Secion II, Secion III inroduces he cooperaive posiioning sysem on which he proposed mehod is based. The proposed wosep sraegy combined wih paricle filer is described in deail in Secions IV, and experimenal resuls are presened in Secion VI.

2 II. RELATED RESEARCH In he roboics communiy, appearance-based localizaion using camera images which is very close o he sensing way of human being has achieved grea success. The BoF echnique is successfully applied o he appearance-based SLAM problem. I firs exracs local feaures of an image and hen consrucs a hisogram using he exraced local feaures as a represenaion of he global feaures. In [2], he loop closure deecion, which is a problem o find a locaion where he robo previously visied, was solved by he BoF echnique in a probabilisic manner. However, hese mehods assume ha he illuminaion condiion does no change so severely. Therefore, he ravel disance of he robo mus be shor enough so ha he lighing condiion will no change drasically. In anoher sudy, he experimenal resuls were no evaluaed explicily under significan changes in illuminaion [3]. These mehods will fail in he dark or in environmens in which he lighing is dramaically changed. When range is measured by a laser range finder, he refleciviy, which indicaes he srengh of he refleced laser, can be obained as a byproduc of range daa. Noe ha all of he pixels in he range image have corresponding reflecance values. In oher words, he range image and he reflecance image are precisely and fundamenally aligned. In addiion, since he reflecance image is no subjec o any exreme variaions in he exernal illuminaion condiions, sable reflecance images can be obained even a nigh. Kara e al.[4] uilizes reflecion inensiy from a 1D laser range finder for localizaion in 2D space. On he oher hand, our echnique uilizes 2D reflecance images obained by a 3D laser range finder for 3D localizaion. The proposed echnique uses a panoramic reflecance image insead of a regular camera image. By applying he BoF echnique for a reflecance image ha corresponds o 3D range daa, he global localizaion using 3D range daa and 2D images is achieved efficienly. Levinson e al.[8] uilize 2D infrared refleciviy map o achieve a real-ime auonomous vehicle navigaion. Firsly hey creaed a spaial grid map wih infrared remiance values in a probabilisic manner using SLAM echnology and deermined he vehicle posiion online wih he 2D infrared refleciviy map and a GPS/IMU sysem. Paricle filer(mone carlo localizaion) is an useful probabilisic inference algorihm[12]. In paricle filer algorihm, he probabiliies of Bayesian inference are represened as many paricles. Paricle filer has been applied in many successful robo sysem. Wolf e al.[16] inegraes Mone carlo localizaion(mcl) wih an image rerieval sysem. Andreasson e al.[17] inegraes Mone carlo localizaion wih an panoramic imaging sysem. Zingarei e al.[19] compares wo differen MCL sysems, one using SIFT-based vision sysem and he oher using sonar sensing sysem, hen i combines hese wo sysem o ge beer localizaion under percepual aliasing condiions. The algorihm framework of he proposed echnique is similar o ha described in [16] and [17] which weigh he paricles according o he resuls of he image rerieval process. However, since hese echniques[16][17] depend on convenional camera images, i apparenly would fail under severe variaion of illuminaion. III. DEVELOPMENT OF A 3D GLOBAL MAP BY THE COOPERATIVE POSITIONING SYSTEM A. Laser-based environmenal modeling by muliple mobile robos For map-based global localizaion, an environmenal map mus be creaed and provided beforehand. For precise 3D mapping of he environmen around a robo, we have proposed an efficien and precise sysem called CPS-SLAM [5], which can consruc a raher accurae large-scale 3D map by means of a laser range finder and muliple robos based on echnology used for geographical surveying. This echnique is used as he basis of he urban search and rescue (USAR) robo [6]. Fig. 1. Three-dimensional modeling robos, CPS-VI TABLE I LASER RANGE FINDER (SICK LMS151) Measuring range Field of view Precision Angular resoluion 50[m] 270[ ] ±30[mm] 0.25[ ] Figure 1 shows he sixh CPS-SLAM model, called CPS- VI. This sysem consiss of one paren robo and wo child robos. The paren robo is equipped wih a highly precise laser range finder (GPT-9000A, TOPCON LTD), a 2D laser range finder (SICK LMS151), and a hree-axis aiude sensor. The wo child robos are equipped wih corner cubes. The GPT-9000A and corner cubes are used cooperaively for self-posiioning, as shown in Fig. 2. The LMS151 (Table I) placed on a roaing able acquires wo-dimensional sli-like range daa, which are verical o he ground. This sensor can capure reflecance daa a he same ime. Therefore, by roaing he able around he verical axis for 360 while scanning wih a 2D laser range finder, 3D range daa and

3 a 2D reflecance image are acquired. The number of pixels on a reflecance image is exacly he same as he number of 3D poins in he range daa, i.e., here exiss a one-o-one mapping relaionship beween 2D pixels of he reflecance image and 3D poins of he local 3D map. Fig. 4. Reflecance image and camera image in corridor Fig. 5. Two sideviews of local 3D map used for appearance-based global localizaion in he proposed wo-sep sraegy. Fig. 3. Fig. 2. Cooperaive posiioning sysem (CPS) Consrucion of a large-scale 3D map by he CPS B. Three-dimensional global map The process of mapping he enire field is displayed in Fig. 3. In each locaion, he paren robo collecs a local 3D map and is measuremen posiion based on he relaive observaion beween he paren and child robos. Evenually, all of he local 3D maps are aligned ino a global 3D map using he measuremen posiion informaion. Addiional deails abou CPS-based simulaneous localizaion and mapping (CPS-SLAM) can be found in [5]. C. Reflecance images As menioned above, reflecance images are capured as a byproduc of range sensing. An examples of a reflecance image and is corresponding camera image aken in he nigh are shown in Fig. 4, Two sideviews of he corresponding 3D daa acquired by CPS-VI are shown in Fig. 5. Noe ha each reflecance image conains he posiion informaion for he locaion a which he image was capured. These images are D. Image rerieval using he BoF echnique When he global 3D map of he arge field is consruced, a Kd-ree srucure soring BoF represenaions of reflecance images is also consruced a he same ime. Reflecance images are represened as hisograms of occurrence of he visual words in an image. SIFT and SURF[20][18] are well used in appearance-based robo localizaion for exracing feaures. Firs, regions in feaure space are mapped o visual words by clusering all SURF or SIFT feaures exraced from recorded images ino represenaive words using k- means clusering, and he words are sored using a Kd-ree srucure. Using hese words as he x-axis, we quanize each feaure in a reflecance image o is approximae neares word by searching he Kd-ree, and all of he recorded reflecance images are represened as saisics of words (hisograms). Finally, he hisograms of all recorded images are sored using a Kd-ree srucure. A newly-capured image is also represened as a hisogram, and M images ha bes mach he newly-capured image are rerieved by quanizing he hisogram o is neares M hisograms. These M candidaes are used as he observaion model of paricle filer. IV. TWO-STEP STRATEGY This secion presens he wo-sep sraegy for precise localizaion using a 3D map. Firs, we need o creae a global map as a raining daase. As explained in Secion III, he CPS robos move in he environmen and consruc a 3D global map. A he same ime, he paren robo collecs reflecance images a each measuremen posiion. Then, all of reflecance images are represened using he BoF echnique, and a raining daase is creaed. Finally, he daase of all of he BoF represenaions is sored in a Kd-ree, which is efficien for informaion rerieval. For global localizaion, a new robo ha is equipped wih a 3D range sensor, such as CPS-VI, collecs local 3D daa and 2D reflecance images (es daa). In he firs sep, we rerieve M iniial locaion candidaes by comparing sored reflecance images (raining daase) and capured reflecance images (es daa) using he BoF echnique and a Kd-ree.

4 Nex, we apply paricle filer for exracing a correc iniial locaion candidae from hese M candidaes. By using he paricle filer, paricles converge o few locaions, and false iniial locaions are excluded. So only a few iniial candidaes are lef for he nex precise localizaion. We hen apply a 3D geomeric consrain in order o exrac rue feaure pairs and run auomaic ICP in he second sep. As a resul of he firs sep, fas and precise localizaion can be performed in he second sep by comparing only one 3D range images, which is seleced based on he similariy of he reflecance images and probabilisic inference in he firs sep. We hereinafer denoe Train i.re f and Train i.ps as he i h reflecance image and he local 3D map, respecively, in he raining daase, and Tes j.re f and Tes j.ps as he j h reflecance image and he local 3D map, respecively, in he es daase. In he following, he proposed wo-sep sraegy is described in deail. A. Firs sep: image rerieval and Paricle filer a) Image rerieval by BoF using 2D reflecance images: All Tes j.re f are convered ino BoF represenaions, and he M bes maches are searched in he Kd-ree ha was consruced from he raining daase. The M Train i.re f and Train i.ps are hen seleced as M candidaes for he posiion of he robo in he 3D global map. b) Paricle filer: When he reflecance images are colleced, he posiions are also recorded by CPS, so a opological graph in which each node is represened by a reflecance image is creaed. By he resuls of image rerieval, M candidaes locaion in he opological graph are used for he observed daa of paricle filer. The paricles will converge o one of M candidaes. The deails are described a secion V. B. Second sep: precise measuremen by auomaic ICP using 3D daa Wih he posiion of paricle afer convergence, auomaic ICP [9][10] which consiss of wo processes is applied. However, before applying auomaic ICP, 3D geomeric consrains are used o remove ouliers. In some cases, he number of false ouliers is large han rue inliers, herefore we have proposed a wo-sep voing algorihm based on 3D geomeric consrains o remove ouliers efficienly in [21]. The process of auomaic ICP is as follows: 1) Rough alignmen wih RANSAC a) Find he corresponding feaures beween Tes j.re f and Train i.re f. b) Ge he 3D coordinaes of corresponding feaures using Tes j.ps and Train i.ps, which correspond o Tes j.re f and Train i.re f, respecively. c) Remove ouliers by wo-sep voing algorihm. d) 3D ransformaion beween Tes j.ps and Train i.ps is esimaed by RANSAC. e) Align Tes j.ps o Train i.ps. 2) Precise alignmen wih ICP a) Run ICP[9] using Tes j.ps and Train i.ps, which are already roughly aligned. V. SIR PARTICLE FILTER Sequenial Imporance Resampling(SIR) paricle filer is a kind of Bayesian filering echniques which esimae he belief recursively. The key idea of paricle filer is represening he probabilisic belief p(x o,a) of robo s locaion by a se of weighed paricles. The probabiliy of curren sae X given a hisory of acion and observaion (abbreviaed as a and o, respecively) is approximaed as: S = {x (i), π (i) }, i = 1...N Each paricle x (i) represens a hypohesized sae wih a nonnegaive numerical weigh π (i). The alernaing hree main seps of SIR paricle filer are as follows: 1) Generae a new se of paricles S +1 by resampling from old paricle se S according o paricle weighs π (i),i = 1...N 2) Predic he nex sae of new paricle se based on he moion model wih addiional Gaussian noise. 3) Calculae he new weighs of new paricles, his is done by inegraing he observaion model ino he weigh of each paricle. The deails how paricle filer is applied in our robo sysem is described as follows: A. Moion model When a robo is visiing he arge environmens, all paricle variables x (i) = (x,y,ϕ) (i) where (x,y) is he robo posiion in 2D grid map and ϕ is he orienaion, are updaed according o he movemen beween adjacen measuremen posiions which are obained by odomery or oher local localizaion echniques such as CPS. Meanwhile he paricle variables are updaed wih small random values drawn from a normal disribuion, using a sandard deviaion of var yaw for he roaion and a sandard deviaion of var x and var y for he ranslaion. B. Observaion model In each locaion, wih he curren scanned reflecance image, M candidae posiions o ( j) from raining daase of reflecance images are searched ou by Kd-ree. In M candidaes only he one whose collecing locaion is closes o he curren paricle is used for calculaing is weigh. From he auomaic ICP, he roaion angle ϕ can be calculaed correcly, i doesn need o be used for calculaing weighs. The weighing funcion is defined as: f w (d) = exp( d2 δ 2 ) (1) d is he minimum Euclidean disance beween x (i), j = 1...M, o ( j) and d = M min j=1 x(i) o ( j) (2)

5 C. Locaion esimaion The paricles will converge o a locaion a each loop in he process of SIR. The convergen paricles locaion is he roughly esimaed robo s posiion. Wih he curren convergen locaion, he reflecance image whose locaion is closes o i is seleced. This means ha wo corresponding reflecance images are found: one is he curren reflecance image, and anoher is he searched reflecance image from raining daase. Wih his wo reflecance images and heir corresponding 3D poins daa, he robo s locaion can be precisely esimaed by auomaic ICP. VI. EXPERIMENT AND RESULTS The experimen is conduced in an environmen wih srong percepual aliasing in order o verify he performance of he proposed wo-sep sraegy. The 2D map for he experimen is shown in Fig. 6 which is creaed in a corridor. There are many similar scenes in his pah, wo pairs of similar scenes are also shown in Fig. 6, examples of reflecance image and corresponding 3D poins daa have been shown in Fig. 4 and Fig. 5. In his experimen, he size of reflecance images is pixels. The CPS-VI robos move and sop a 72 differen locaions in he experimenal area in Fig. 6, i.e. 72 daa (Train i.re f and Train i.ps) are sored as he raining daase. On he oher hand, es daa (Tes j.re f and Tes j.ps) are colleced a 36 locaions in he same map. Boh he raining pah and es pah are shown in Fig. 7. In Eq.(1), δ was se o be 2T where T is he average moving disance beween he adjacen collecing posiions of raining daase. Here T is se o be 3.6[m]. Wih his value of δ, he number of candidaes M (see Eq.(2)) is se o be 6. The paricles converge o he correc locaion afer he 5 h scanning posiion (shown as 04 in Fig.7) in es pah. Table II shows he correcness of he iniial localizaion wihin he 6 candidaes esimaed by BoF, i.e. in which rank he correc locaion is chosen wihin six candidaes rerieved from he raining daase by he Kd-ree for each Tes j.re f. The experimenal resuls show ha 31 locaions in oal are correcly esimaed wihin 6 candidaes for every Tes j.re f. Especially in 19 posiions, he posiions of he robo are correcly esimaed as he firs candidae. In wo posiions, he second candidae and he fourh candidae are he acual locaions, respecively. There are = 5 posiions where he BoF can iniially esimae robo s locaion correcly. So he final accuracy is 31/ %. Noe ha alhough proper iniial locaions are no obained a 5 posiions from he BoF, he correc posiion is esimaed by he auomaic ICP afer 5 h locaion hanks o he paricle filer. Paricle filer excludes false esimaion and finds rue robo s locaion. The efficiency of paricle filer is verified by auomaic ICP. As menioned in [21], four non-coplanar rue pairs of maching 3D poins are enough for calculaion of a rigid ransformaion. By seing he parameers of voing algorihm (see [21] for more deails), all of righ candidae correspondences can be correcly aligned by auomaic ICP. Since afer he locaion 04 (i.e. he 5 h locaion in es pah), all false candidaes are excluded by paricle filer. Therefore, correc corresponding Train i.ps and Tes j.ps are found, and robo s locaion are all correcly esimaed in remaining posiions. Figure 8 shows he comparison beween he localizaion of paricle filer and final precise localizaion by Auomaic ICP. Among he red poins, before locaion 04, paricles don converge o correc locaions. These locaions are excluded by Auo-ICP and corresponding red poins are no shown in Fig.8. Figure 9 shows an example of alignmen beween rain 10.ps es 10.ps and 2D feaures of reflecance images. Their locaions are shown in Fig.7. TABLE II RESULTS FROM SEARCHING IN BOF Fig. 6. Experimenal areas No. 1s 2nd 3rd 4h 5h 6h oal accuracy Correc localizaion % Fig. 7. Experimenal pahs, red line is he raining pah. blue line is he es pah. VII. CONCLUSION We proposed and demonsraed a wo-sep sraegy combined wih paricle filer for global localizaion of a mobile robo in environmens wih srong percepual aliasing. Besides he combinaion of appearance-based global localizaion and map-based global localizaion which improve he performance of correc posiion esimaion, paricle filer

6 Fig. 8. Comparison beween he paricle filer locaion and alignmen of auomaic ICP. Fig. 9. From righ o lef, hen op o boom: Original 3D poins; Roughly aligned 3D poins by voing algorihm and RANSAC; Precisely aligned 3D poins by ICP; Mached feaures in reflecance images is used for furher excluding false iniial localizaion in appearance-based localizaion. The reflecance image, which is obained as a byproduc of range sensing and is independen of he variaion of he illuminaion condiion, is used for appearance-based global localizaion in he firs sep. Precise map-based global localizaion by ICP is applied using 3D local maps, which are seleced by he firs sep. The effeciveness of he proposed echnique is demonsraed hrough experimens in an indoor image sequence wih srong percepual aliasing. VIII. ACKNOWLEDGMENTS The presen sudy was suppored in par by a Gran-in-Aid for Scienific Research (B) ( ). REFERENCES [1] D. Fillia, J.-A. Meyer, Map-based navigaion in mobile robos: I. A review of localizaion sraegies, Cogniive Sysems Research, vol. 4, no. 4, pp , [2] A. Angeli, D. Fillia, S. Doncieux, and J.A. Meyer, A Fas and Incremenal Mehod for Loop-Closure Deecion Using Bags of Visual Words, IEEE Transacions on Roboics, vol. 24, no.5, pp , [3] M. Cummins, P. Newman, FAB-MAP: Probabilisic Localizaion and Mapping in he Space of Appearance, Inernaional Journal of Roboics Research, vol. 27, no. 6, pp , [4] Y. Hara, H. Kawaa, A. Ohya and S. Yua, Mobile Robo Localizaion and Mapping by Scan Maching using Laser Reflecion Inensiy of he SOKUIKI Sensor, IEEE Indusrial Elecronics, IECON, pp , [5] R. Kurazume, S. Hirose, An Experimenal Sudy of a Cooperaive Posiioning Sysem, Journal of Auonomous Robos, vol. 8, no. 1, pp , [6] M. Guarnieri, R. Kurazume, H. Masuda, HELIOS sysem: A eam of racked robos for special urban search and rescue operaions, IEEE/RSJ Inernaional Conference on Inelligen Robos and Sysems, IROS, pp , [7] S. Thrun, S. Thayer, W. Whiaker e al, Auonomous exploraion and mapping of abandoned mines: Sofware archiecure of an auonomous roboic sysem, IEEE Roboics and Auomaion Magazine, vol. 11, no. 4, pp , [8] J. Levinson, S. Thrun, Robus vehicle localizaion in urban environmens using probabilisic maps, in Proc. IEEE Inernaional Conference on Roboics and Auomaion, pp , [9] P. J. Besl, N. D. McKay, A mehod for regisraion of 3-D shapes, IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 14, no. 2, pp , [10] C. S. Chen, Y. P. Hung, RANSAC-Based DARCES: A new approach o fas auomaic regisraion of parially overlapping range images, IEEE Transacions on Paern Analysis and Machine Inelligence, vol. 21, no. 11, pp , [11] F. Bonin-Fon, A. Oriz and G. Oliver, Visual Navigaion for Mobile Robos: A Survey, Journal of Inelligen and Roboic Sysems: Theory and Applicaions, vol. 53, no. 3, pp , [12] S. Thrun, D. Fox, W. Bugard and F. Dellaer, Robus Mone Carlo localizaion for mobile robos, Arificial Inelligence, vol. 128, pp , [13] Y. Masumoo, M. Inaba, H. Inoue, Visual navigaion using viewsequenced roue represenaion, in Proc. IEEE Inernaional Conference on Roboics and Auomaion, vol. 1, pp , [14] T. Ohno, A. Ohya, S. Yua, Auonomous navigaion for mobile robos referring pre-recorded image sequence, IEEE Inernaional Conference on Inelligen Robos and Sysems, vol. 2, pp , [15] E. Menegai, M. Zoccarao, E. Pagello, H. Ishiguro, Image-based Mone Carlo localisaion wih omnidirecional images, Roboics and Auonomous Sysems, vol. 48, no. 1, pp , [16] J. Wolf, W. Burgard, H. Burkhard, Robus vision-based localizaion for mobile robos using an image rerieval sysem based on invarian feaures, IEEE Inernaional Conference on Roboics and Auomaion, vol. 1, pp , [17] H. Andreasson, A. Trepow, T. Ducke, Self-localizaion in nonsaionary environmens using omni-direcional vision, Roboics and Auonomous Sysems, vol. 55, no. 7, pp , [18] A.C. Murillo, J.J. Guerrero, C. Sagüés, SURF feaures for efficien robo localizaion wih omnidirecional images, IEEE Inernaional Conference on Roboics and Auomaion, pp , [19] P. Zingarei, E. Frononi, Vision and sonar sensor fusion for mobile robo localizaion in aliased environmens, IEEE/ASME Inernaional Conference on Mecharonic and Embedded Sysems and Applicaions, pp , [20] C. Valgren, A.J. Lilienhal, SIFT, SURF & seasons: Appearancebased long-erm localizaion in oudoor environmens, Roboics and Auonomous Sysems, vol. 58, no. 2, pp , [21] D. X. Zhang, R. kurazume, Y. Iwashia, T. Hasegawa, Appearance and map-based global localizaion using laser refleciviy, in Proc. IEEE Inernaional Conference on Roboics and Biomimeics, o be appeared, 2011.