Abstract
A mobile robot needs to perceive the motions of external objects to perform tasks successfully in a dynamic environment. We propose a set of algorithms for multiple motion tracking from a mobile robot equipped with a monocular camera and a laser rangefinder. The key challenges are 1. to compensate the ego-motion of the robot for external motion detection, and 2. to cope with transient and structural noise for robust motion tracking. In our algorithms, the robot ego-motion is directly estimated using corresponding feature sets in two consecutive images, and the position and velocity of a moving object is estimated in image space using multiple particle filters. The estimates are fused with the depth information from the laser rangefinder to estimate the partial 3D position. The proposed algorithms have been tested with various configurations in outdoor environments. The algorithms were deployed on three different platforms; it was shown that various type of ego-motion were successfully eliminated and the particle filters were able to track motions robustly. The real-time capability of the tracking algorithm was demonstrated by integrating it into a robot control loop.
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Bajracharya M, Moghaddam B, Howard A, Brennan S, Matthies LH (2009) Results from a real-time stereo-based pedestrian detection system on a moving vehicle. In: Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking
Barfoot TD (2005) Online visual motion estimation using FastSLAM with SIFT features. In: Proceedings of the 2005 IEEE/RSJ international conference on intelligent robots and systems, Alberta, Canada, August 2005, pp 579–585
Behrad A, Shahrokni A, Motamedi SA (2001) A robust vision-based moving target detection and tracking system. In: The proceeding of image and vision computing conference, University of Otago, Dunedin, November 2001
Black MJ, Anandan P (1996) The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput Vis Image Underst 63(1):75–104
Bouguet J-Y (1999) Pyramidal implementation of the Lucas Kanade feature tracker: Description of the algorithm. Technical report, Intel Research Laboratory
Censi A, Fusiello A, Roberto V (1999) Image stabilization by features tracking. In: Proceedings of the 10th international conference on image analysis and processing, Venice, Italy, September 1999, pp 665–667
Cohen I, Medioni G (1999) Detecting and tracking objects in video surveillance. In: Proceedings of the IEEE computer vision and pattern recognition 99, Fort Collins, June 1999, pp 319–325
Collins R, Lipton A, Fujiyoshi H, Kanade T (2001) Algorithms for cooperative multisensor surveillance. In: Proceedings of the IEEE, vol 89, pp 1456–1477, October 2001
Cox IJ, Hingorani SL (1996) An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans Pattern Anal Mach Intell 18(2):138–150
Ess A, Schindler K, Leibe B, van Gool L (2009) Improved multi-person tracking with active occlusion handling. In: Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking
Foresti GL, Micheloni C (2003) A robust feature tracker for active surveillance of outdoor scenes. Electron Lett Comput Vis Image Anal 1(1):21–34
Forsyth DA, Ponce J (2003) Computer vision: a modern approach. Prentice Hall, Englewood Cliffs
Fox D (2001) KLD-sampling: Adaptive particle filter. In: Advances in neural information processing systems 14. MIT Press, Cambridge
Gutmann J-S, Schlegel C (1996) Amos: Comparison of scan matching approaches for self-localization in indoor environments. In: Proceedings of the 1st Euromicro workshop on advanced mobile robots, pp 61–67
Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the fourth Alvey vision conference, Manchester, pp 147–151
Hue C, Le Cadre J-P, Pérez P (2001) A particle filter to track multiple objects. In: IEEE workshop on multi-object tracking, Vancouver, Canada, July 2001, pp 61–68
Irani M, Anandan P (1998) Video indexing based on mosaic representation. IEEE Trans Pattern Anal Mach Intell 86(5):905–921
Irani M, Rousso R, Peleg S (1994) Recovery of ego-motion using image stabilization. In: Proceedings of the IEEE computer vision and pattern recognition, March 1994, pp 454–460
Isard M, Blake A (1998) Condensation—conditional density propagation for visual tracking. Int J Comput Vis 29(1):5–28
Jung B (2005) Cooperative target tracking using mobile robots. PhD thesis, University of Southern California, Los Angeles, CA
Jung B, Sukhatme GS (2004) Detecting moving objects using a single camera on a mobile robot in an outdoor environment. In: International conference on intelligent autonomous systems, The Netherlands, March 2004, pp 980–987
Kang J, Cohen I, Medioni G (2002) Continuous multi-views tracking using tensor voting. In: Proceedings of the IEEE workshop on motion and video computing, Orlando, Florida, December 2002, pp 181–186
Kang J, Cohen I, Medioni G (2003) Continuous tracking within and across camera streams. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, Madison, Wisconsin, June 2003, pp 267–272
Ke Y, Sukthankar R (2004) PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of the IEEE computer vision and pattern recognition, Washington, DC, June 2004, pp 506–513
Kelly A (1994) A 3D state space formulation of a navigation Kalman filter for autonomous vehicles. Technical Report CMU-RI-TR-94-19, The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, May 1994
Lau B, Arras KO, Burgard W (2009) Multi-modal hypothesis group tracking and, group size estimation. In: Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110
Lu F, Milios E (1997) Robot pose estimation in unknown environments by matching 2D range scans. J Intell Robot Syst 18:249–275
Luber M, Arras KO, Plagemann C, Burgard W (2009) Classifying dynamic objects: An unsupervised learning approach. Auton Robot 26(2–3):141–151
Luber M, Tipaldi GD, Arras KO (2009) Spatially grounded multi-hypothesis tracking of people. In: Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking
Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th international joint conference on artificial intelligence, pp 674–697
Maccormick J, Blake A (2000) A probabilistic exclusion principle for tracking multiple objects. Int J Comput Vis 39(1):57–71
Murray D, Basu A (1994) Motion tracking with an active camera. IEEE Trans Pattern Anal Mach Intell 16(5):449–459
Nordlund P, Uhlin T (1996) Closing the loop: Detection and pursuit of a moving object by a moving observer. Image Vis Comput 14:265–275
Rasmussen C, Hager GD (2001) Probabilistic data association methods for tracking complex visual objects. IEEE Trans Pattern Anal Mach Intell 23(6):560–576
Sclaroff S, Rosales R (1999) 3d trajectory recovery for tracking multiple objects and trajectory guided recognition of actions. In: IEEE conference on computer vision and pattern recognition, pp 117–123
Rowe S, Blake A (1996) Statistical mosaics for tracking. J Image Vis Comput 14:549–564
Saripalli S, Montgomery JF, Sukhatme GS (2003) Visually-guided landing of an unmanned aerial vehicle. IEEE Trans Robot Autom 19(3):371–381
Saripalli S, Roberts JM, Corke PI, Buskey G, Sukhatme GS (2003) A tale of two helicopters. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, October 2003, pp 805–810
Schiele B, Andriluka M, Majer N, Roth S, Wojek C (2009) Visual people detection—different models, comparison and discussion. In: Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking
Se S, Barfoot T, Jasiobedzki P (2001) Visual motion estimation and terrain modeling for planetary rovers. In: IEEE international conference on robotics and automation, South Korea, May 2001, pp 2051–2058
Shi J, Tomasi C (1994) Good features to track. In: Proceedings of IEEE conference on computer vision and pattern recognition, Seattle, Washington, June 1994, pp 593–600
Spinello L, Triebel R, Siegwart R (2009) A trained system for multimodal perception in urban environments. In: Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking
Srinivasan S, Chellappa R (1997) Image stabilization and mosaicking using the overlapped basis optical flow field. In: Proceedings of IEEE international conference on image processing, October 1997, pp 356–359
Stauffer C, Grimson EL (2000) Learning patterns of activity using real-time tracking. IEEE Trans Pattern Anal Mach Intell 22(8):747–757
Sullivan J, Nillius P, Carlsson S (2009) Multi-target tracking on a large scale: Experiences from football player tracking. In: Proceedings of the IEEE ICRA 2009 workshop on people detection and tracking
Thrun S, Fox D, Burgard W, Dellaert F (2001) Robust Monte Carlo localization for mobile robots. Artif Intell 128:99–141
Tomasi C, Kanade T (1991) Detection and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University, Pittsburgh, PA, April 1991
Ulrich I, Borenstein J (1998) VFH+: Reliable obstacle avoidance for fast mobile robots. In: Proceeding of the IEEE international conference on robotics and automation, Leuven, Belgium, 16–21 May 1998, pp 1572–1577
van Leeuwen MB, Groen FCA (2002) Motion interpretation for in-car vision systems. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems. EPFL, Lausanne, pp 135–140
Vermaak J, Doucet A, Perez P (2003) Maintaining multi-modality through mixture tracking. In: Proceedings of the 9th IEEE international conference on computer vision, pp 1110–1116
Vidal R (2005) Multi-subspace methods for motion segmentation from affine, perspective and central panoramic cameras. In: Proceedings of the 2005 IEEE international conference on robotics and automation, pp 1216–1221
Wang JYA, Adelson EH (2004) Representing moving images with layers. IEEE Trans Image Process 3(5):625–638
Welch G, Bishop G (2006) An introduction to the Kalman filter. Technical Report 95-041, Department of Computer Science, University of North Carolina at Chapel Hill
Wren CR, Azarbayejani A, Darrell T, Pentland A (1997) Pfinder: Real-time tracking of the human body. IEEE Trans Pattern Anal Mach Intell 19(7):780–785
Xiao J, Shah M (2005) Accurate motion layer segmentation and matting. In: Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition, Washington, DC, 2005, pp 698–703
Yilmaz A, Shafique K, Lobo N, Li X, Olson T, Shah M (2001) Target-tracking in FLIR imagery using mean-shift and global motion compensation. In: Workshop on computer vision beyond the visible spectrum, Kauai, Hawaii, December 2001, pp 54–58
Zoghlami I, Faugeras O, Deriche R (1997) Using geometric corners to build a 2D mosaic from a set of images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 420–425
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Jung, B., Sukhatme, G.S. Real-time Motion Tracking from a Mobile Robot. Int J of Soc Robotics 2, 63–78 (2010). https://doi.org/10.1007/s12369-009-0038-y
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DOI: https://doi.org/10.1007/s12369-009-0038-y