Novel Rao-Blackwellized Particle Filter for Mobile Robot SLAM Using Monocular Vision
This paper presents the novel Rao-Blackwellised
particle filter (RBPF) for mobile robot simultaneous localization and
mapping (SLAM) using monocular vision. The particle filter is
combined with unscented Kalman filter (UKF) to extending the path
posterior by sampling new poses that integrate the current observation
which drastically reduces the uncertainty about the robot pose. The
landmark position estimation and update is also implemented through
UKF. Furthermore, the number of resampling steps is determined
adaptively, which seriously reduces the particle depletion problem,
and introducing the evolution strategies (ES) for avoiding particle
impoverishment. The 3D natural point landmarks are structured with
matching Scale Invariant Feature Transform (SIFT) feature pairs. The
matching for multi-dimension SIFT features is implemented with a
KD-Tree in the time cost of O(log2
N). Experiment results on real robot
in our indoor environment show the advantages of our methods over
previous approaches.
[1] D. Kortenkamp, R.P. Bonasso, and R. Murphy, editors, AI-based Mobile
Robots: Case studies of successful robot systems, MIT Press, Cambridge,
1998, pp. 91-122.
[2] R. C. Smith, P. Cheeseman, "On the Representation and Estimation of
Spatial Uncertainty," International Journal of Robotics Research, vol. 5,
no. 4, pp. 56-68, 1986.
[3] J. Leonard, J. D. Tard'os, S. Thrun, and H. Choset, editors, Workshop
Notes of the ICRA Workshop on Concurrent Mapping and Localization
for Autonomous Mobile Robots, in Proc. IEEE Int. Conf. Robotics and
Automation, Washington, DC, 2002.
[4] J. E. Guivant, E. M. Nebot, "Optimization of the simultaneous
localization and map-building algorithm for real-time implementation,"
IEEE Trans. Robotics and Automation, vol. 17, no. 3, pp. 242-257, 2001.
[5] A. J. Davison and D. W. Murray, "Simultaneous localization and map
building using active vision," IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 24, no. 7, pp. 865-880, 2002.
[6] K. Murphy and S. Russell, "Rao-blackwellized particle filtering for
dynamic bayesian networks," in Sequential monte carlo methods in
practice, Springer Verlag, 2001.
[7] M. Montemerlo and S. Thrun, "Simultaneous localization and mapping
with unknown data association using FastSLAM," in Proc. IEEE Int.
Conf. Robotics and Automation, Taipei, 2003.
[8] A. J. Davison, "Real-time simultaneous localisation and mapping with a
single camera," in Proc. of The Ninth Int. Conf. on Computer Vision
ICCV'03, Nice, France, 2003, pp. 1403-1410.
[9] C. Stachniss, G. Grisetti, and W. Burgard, "Recovering Particle Diversity
in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing
Loops," in Proc. IEEE Int. Conf. Robotics and Automation, 2005, pp.
667-672, Barcelona, Spain.
[10] R. Sim, P. Elinas, M. Griffin, and J. Little, "Vision-based SLAM using the
Rao-Blackwellized Particle Filter," in Workshop Reasoning with
Uncertainty in Robotics, Edinburgh, Scotland, 2005.
[11] M. N. Dailey and M. Parnichkun, "Landmark-based simultaneous
localization and mapping with stereo vision," in Proc. of the 2005 Asian
Conf. on Industrial Automation and Robotics, 2005.
[12] S. Se, D. Lowe, and J. Little, Mobile robot localization and mapping with
uncertainty using scale-invariant visual landmarks, International Journal
of Robotics Research, 21(8): 735-758, 2002.
[13] A. Davison, Y. Cid, and N. Kita, "Real-time 3D SLAM with wide-angle
vision," in Proceedings of the IFAC Symposium on Intelligent
Autonomous Vehicles, 2004.
[14] D. Lowe, "Distinctive image features from scale-invariant keypoints," Int.
J. of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[15] A. W. Moore, "An introductory tutorial on kd-trees," Robotics Institute,
Carnegie Mellon University, Pittsburgh, Technical Report No. 209,
Computer Laboratory, University of Cambridge, 1991.
[16] R. Merwe, A. Doucet, N. Freitas, and E. Wan, "The Unscented Particle
Filter," Technical Report CUED/FINFENG /TR 380, Cambridge
University, Engineering Department, 2000.
[17] A. Doucet, "On sequential simulation-based methods for Bayesian
filtering," Technical report, Signal Processing Group, Departement of
Engeneering, University of Cambridge, 1998.
[18] T. Duckett, "A genetic algorithm for simultaneous localization and
mapping," in Proc. of the IEEE International Conference on Robotics and
Automation, 2003, pp. 434-439.
[19] J. S. Liu and R. Chen, "Sequential Monte Carlo methods for dynamical
systems," J. Amer. Statist. Assoc., vol. 93, pp. 1032-1044, 1998.
[20] Z. Zhang, "Flexible camera calibration by viewing a plane from unknown
orientations," Proc. ICCV, pp. 666-671, 1999.
[1] D. Kortenkamp, R.P. Bonasso, and R. Murphy, editors, AI-based Mobile
Robots: Case studies of successful robot systems, MIT Press, Cambridge,
1998, pp. 91-122.
[2] R. C. Smith, P. Cheeseman, "On the Representation and Estimation of
Spatial Uncertainty," International Journal of Robotics Research, vol. 5,
no. 4, pp. 56-68, 1986.
[3] J. Leonard, J. D. Tard'os, S. Thrun, and H. Choset, editors, Workshop
Notes of the ICRA Workshop on Concurrent Mapping and Localization
for Autonomous Mobile Robots, in Proc. IEEE Int. Conf. Robotics and
Automation, Washington, DC, 2002.
[4] J. E. Guivant, E. M. Nebot, "Optimization of the simultaneous
localization and map-building algorithm for real-time implementation,"
IEEE Trans. Robotics and Automation, vol. 17, no. 3, pp. 242-257, 2001.
[5] A. J. Davison and D. W. Murray, "Simultaneous localization and map
building using active vision," IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 24, no. 7, pp. 865-880, 2002.
[6] K. Murphy and S. Russell, "Rao-blackwellized particle filtering for
dynamic bayesian networks," in Sequential monte carlo methods in
practice, Springer Verlag, 2001.
[7] M. Montemerlo and S. Thrun, "Simultaneous localization and mapping
with unknown data association using FastSLAM," in Proc. IEEE Int.
Conf. Robotics and Automation, Taipei, 2003.
[8] A. J. Davison, "Real-time simultaneous localisation and mapping with a
single camera," in Proc. of The Ninth Int. Conf. on Computer Vision
ICCV'03, Nice, France, 2003, pp. 1403-1410.
[9] C. Stachniss, G. Grisetti, and W. Burgard, "Recovering Particle Diversity
in a Rao-Blackwellized Particle Filter for SLAM After Actively Closing
Loops," in Proc. IEEE Int. Conf. Robotics and Automation, 2005, pp.
667-672, Barcelona, Spain.
[10] R. Sim, P. Elinas, M. Griffin, and J. Little, "Vision-based SLAM using the
Rao-Blackwellized Particle Filter," in Workshop Reasoning with
Uncertainty in Robotics, Edinburgh, Scotland, 2005.
[11] M. N. Dailey and M. Parnichkun, "Landmark-based simultaneous
localization and mapping with stereo vision," in Proc. of the 2005 Asian
Conf. on Industrial Automation and Robotics, 2005.
[12] S. Se, D. Lowe, and J. Little, Mobile robot localization and mapping with
uncertainty using scale-invariant visual landmarks, International Journal
of Robotics Research, 21(8): 735-758, 2002.
[13] A. Davison, Y. Cid, and N. Kita, "Real-time 3D SLAM with wide-angle
vision," in Proceedings of the IFAC Symposium on Intelligent
Autonomous Vehicles, 2004.
[14] D. Lowe, "Distinctive image features from scale-invariant keypoints," Int.
J. of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[15] A. W. Moore, "An introductory tutorial on kd-trees," Robotics Institute,
Carnegie Mellon University, Pittsburgh, Technical Report No. 209,
Computer Laboratory, University of Cambridge, 1991.
[16] R. Merwe, A. Doucet, N. Freitas, and E. Wan, "The Unscented Particle
Filter," Technical Report CUED/FINFENG /TR 380, Cambridge
University, Engineering Department, 2000.
[17] A. Doucet, "On sequential simulation-based methods for Bayesian
filtering," Technical report, Signal Processing Group, Departement of
Engeneering, University of Cambridge, 1998.
[18] T. Duckett, "A genetic algorithm for simultaneous localization and
mapping," in Proc. of the IEEE International Conference on Robotics and
Automation, 2003, pp. 434-439.
[19] J. S. Liu and R. Chen, "Sequential Monte Carlo methods for dynamical
systems," J. Amer. Statist. Assoc., vol. 93, pp. 1032-1044, 1998.
[20] Z. Zhang, "Flexible camera calibration by viewing a plane from unknown
orientations," Proc. ICCV, pp. 666-671, 1999.
@article{"International Journal of Information, Control and Computer Sciences:64273", author = "Maohai Li and Bingrong Hong and Zesu Cai and Ronghua Luo", title = "Novel Rao-Blackwellized Particle Filter for Mobile Robot SLAM Using Monocular Vision", abstract = "This paper presents the novel Rao-Blackwellised
particle filter (RBPF) for mobile robot simultaneous localization and
mapping (SLAM) using monocular vision. The particle filter is
combined with unscented Kalman filter (UKF) to extending the path
posterior by sampling new poses that integrate the current observation
which drastically reduces the uncertainty about the robot pose. The
landmark position estimation and update is also implemented through
UKF. Furthermore, the number of resampling steps is determined
adaptively, which seriously reduces the particle depletion problem,
and introducing the evolution strategies (ES) for avoiding particle
impoverishment. The 3D natural point landmarks are structured with
matching Scale Invariant Feature Transform (SIFT) feature pairs. The
matching for multi-dimension SIFT features is implemented with a
KD-Tree in the time cost of O(log2
N). Experiment results on real robot
in our indoor environment show the advantages of our methods over
previous approaches.", keywords = "Mobile robot, simultaneous localization and
mapping, Rao-Blackwellised particle filter, evolution strategies, scale
invariant feature transform.", volume = "2", number = "3", pages = "963-7", }
