Visual compensation in localization of a robot on a ceiling map

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1 Scientific Research and Essas Vol. ), pp. -, Januar, Available online at ISSN - Academic Journals Full Length Research Paper Visual compensation in localiation of a robot on a map T. Matsumoto, T. Takahashi, M. Iwahashi, T. Kimura, S. Salbiah and N. Mokhtar * Facult of Engineering, Nagaoka Universit of Technolog, Nagaoka, Niigata, Japan. Department of Electrical Engineering, Facult of Engineering, Universit of Malaa, Kuala Lumpur, Malasia. Accepted November, In a visual odometr sstem, location of a mobile robot is automaticall estimated localied) from video. When the video is captured b an "upward" fied to an indoor mobile robot, a panorama image of the map) is generated b using a visual motion between two adjacent s in the video. Similarl, location of another robot can be estimated on the map b using a visual motion between the current and the previousl generated map. Under the assumption that the robot goes straight or rotates around a fied point, there is no problem on the localiation as far as the is flat. However, when there is debris on the, the estimated location contains error. In this paper, we reduce this error b utiliing visual motions in video from the "forward" fied to the robot. This is a visual compensation of motions in the "upward" 's video with those in the "forward" 's video. It was eperimentall confirmed that the maimum absolute value of the error was reduced to approimatel %. Ke words: Visual odometr, localiation, robot vision, motion estimation. INTRODUCTION Auto-localiation of a moving robot is a challenging problem for engineers to develop a robot vision network, in which various kinds of mobile robots communicate and cooperate with each other automaticall. In a situation such that a global positioning sstem GPS) is not applicable, various inner robot sensors such as an acceleration sensor, a geomagnetic attitude sensor, a laser range finder and a groscope are useful for this purpose. Comparing to these sensors, video based localiation methods are less accurate and less robust in general. However, it is still attractive for constructing an energ saving small mobile robot with micro CCD sensors. It is becoming useful owing to recentl developed odometr and simultaneous localiation and mapping SLAM) techniques Desoua and Kak, ; Nister et al., ; Munguia and Grau, ). In this paper, we deal with a sstem in which location of a mobile robot is automaticall estimated localied) from *Corresponding author. norrimamokhtar@um.edu.m. video. When the video is captured b an "upward" fied to an indoor mobile robot, a panorama image of the a map) is generated b using a visual motion motion vector) between two adjacent s in the video. Similarl, location of another robot can be estimated on the map b using a motion vector between the current and the previousl generated map Papanikolopoulos et al., ). Under the assumption that the robot goes straight or rotates around a fied point, there is no problem on the localiation as far as the is flat Udomsiri et al., ). However, when there is debris on the, the estimated location contains error. In this paper, we reduce this error b utiliing motion vectors in video from the "forward" fied to the robot. This is a visual compensation of motions in the "upward" 's video with those in the "forward" 's video. We theoreticall anale relation between kinetic movements of a robot and motion vectors observed in videos from the upward and the forward. To estimate a motion vector, correlation based matching technique has been utilied not onl for MPEG video data compression standard, but also for map generation and

2 Sci. Res. Essas estimated locations estimated location error T error T upward forward debris upward forward debris a) side view front view Figure. Localiation of a robot: a) side view, front view. controlled movement occurred turbulence kinetic movement of a robot T visual motion in video of the upward X Y T X Z T X T Y visual motion in video of the forward X* Y* T Y* T X* Z* Figure. Movements of a robot and their corresponding visual motions in video. localiation Wilson and Theriot, ). However it is sensitive to lighting conditions. Therefore, we utilie the invariant phase onl correlation RI-POC) technique for stable and precise motion estimation Sasaki et al., ; Ito et al., ). OVERVIEW OF THE SYSTEM AND ITS PROBLEM Figure illustrates an indoor mobile robot. It is controlled to go straight or rotate around a fied point on a flat under a flat. The robot has two video s - upward and forward. It is our purpose to automaticall and precisel estimate its location localiation) from the videos. Firstl, a panorama image of the a map) is generated b using a visual motion motion vector) between two adjacent s in video from the upward. Secondl, location of another robot is estimated on the map b using a motion vector between the current and the previousl generated map. Under the assumption that the robot goes straight or rotates around a fied point, there is no problem on the localiation as far as the is flat. However, when there is debris on the as illustrated in the figure, the estimated location contains error T and/or T. ANALYSIS AND THE PROPOSED METHOD Analsis on kinetic movements and visual motions Figure summaries relation between kinetic movements of a robot and visual motions motion vectors) observed in videos from the two video s. Under the assumption in the previous section, a robot is controlled to have onl two kinetic movements - T and. In this case, in video from the upward, T X and Z are observed. It determines the

3 Matsumoto et al. C T It is our purpose to compensate this error for precise estimation of movement and location of a mobile robot. VISUAL COMPENSATION h d a Figure. Modeling of distances and angles. a) upward d d + a + In this paper, we utilied visual motions in video from the "forward" to compensate the error. This is a visual compensation of motions in the "upward" 's video with those in the "forward" 's video. We estimate the visual motions T X*, Z*) in the forward 's video to calculate the errors T, T ) for precise estimation of T, ). Figure illustrates a model for theoretical analsis on our visual compensation. According to the figure, we have a relation between parameters as h+ C + )cosθ ). ) T = h a sin θ ). ) In the proposed method, we calculate the turbulences, ) in the equation above from the visual motions T X*, Z*) estimated b using video from the forward. The estimation is carried out b the invariant phase onl correlation RI-POC) described in [,]. As a result of utiliation of the forward 's video, it is epected to have reduced errors in localiation of a robot on the map. EXPERIMENTAL RESULTS Similarl, we have c) ) T = h a sin θ ) )sinθ where the distances a, d and h are previousl measured constant values. This equation determines the error as = forward d + a In the following eperiments, the parameters in Figure are a = cm, d = cm, and h = cm, respectivel. The robot goes straight at a constant speed. Eample Figure. Eperiment. a) side view before c) after. controlled movements T and without an error. However, if there is debris on the, the robot has two turbulences - and. Each of them generates error T X and T Y respectivel. In this case, miture of TX and T X is observed in X direction in the video from the upward. Those are not separable b using onl the upward. Figure a illustrates side view of this eperiment. The debris brings about the error T to be eliminated b the proposed method. Figure b and c illustrate the estimated locations on a map before and after appling the proposed method respectivel. In Figure c, locations are plotted in a line at a constant interval as the errors are eliminated and the locations are compensated. Figure summaries the error T for the case in Figure. It is observed that the maimum absolute value of the

4 Sci. Res. Essas a) Error T. [cm] error T [cm] error T [cm] Error T. [cm] Figure. Error in the eperiment. c) Error T. [cm] error T [cm] a) after before Figure. Errors in the eperiment. a) direction direction. Eample d) Figure a and b illustrate top view and side view of this eperiment respectivel. The debris brings about the error T and also T. Figure c and d illustrate the estimated locations on a map before and after appling the proposed method respectivel. In Figure d, locations are plotted in a line at a constant interval as the errors are eliminated. Figure a and b summaries the error T and T for the case in Figure. It is observed that the maimum absolute value of the error is reduced from to cm and from to cm respectivel. It is observed that the proposed method can reduce the error to. and.% respectivel. It is confirmed that utiliation of the forward 's video reduced the error to approimatel %. Figure. Eperiment. a) top view side view c) before d) after. Conclusions error is reduced from to cm. It is confirmed that the proposed method can reduce the error to.%. In this paper, we reduced errors in estimating location of an indoor mobile robot on a map b utiliing visual

5 Matsumoto et al. compensation of motions in the "upward" 's video with those in the "forward" 's video. It was eperimentall confirmed that the maimum absolute value of the error was reduced to approimatel %. Future work such as modification of the current mathematical model or other approach will be done to enhance current estimation performance. REFERENCES Desoua GN, Kak AC ). Vision for Mobile Robot Navigation: A Surve. IEEE Trans. Pattern Anal. Machine Intell., ): -. Ito K, Nakajima H, Kobaashi K, Aoki T, Higuchi T ). A Fingerprint Matching Algorithm Using Phase-Onl Correlation. IEICE Trans. Fundam., E-A): -. Munguia R, Grau A ). Monocular SLAM for Visual Odometr. IEEE International Smposium on Intelligent Signal Processing WISP), pp. -. Nister D, Naroditsk O, Bergen J ). Visual Odometr. Proc. IEEE Computer Soc. Conf. Computer Vision Pattern Recognition CVPR), ): I--I-. Papanikolopoulos NP, Khosla PK, Kanade T ). Visual tracking of a moving target b a mounted on a robot: A combination of control and vision. IEEE Trans. Robot. Automation, ): -. Sasaki H, Kobaashi K, Aoki T, Kawamata M, Higuchi T ). Rotation measurements using invariant phase-onl correlation. ITE Tech. Rep., : -. Udomsiri S, Taguchi H, Takahashi T, Iwahashi M, Kimura T ). Functionall Laered Video Coding Based on JPK for Robot Vision Network. J. Robot. Mechatronics, :. Wilson CA, Theriot JA ). A Correlation-Based Approach to Calculate Rotation and Translation of Moving Cells. IEEE Trans. Image Process., ): -.