Efficient probabilistic Range-Only SLAM

This work addresses range-only SLAM (RO-SLAM) as the Bayesian inference problem of sequentially tracking a vehicle while estimating the location of a set of beacons without any prior information. The only assumptions are the availability of odometry and a range sensor able of identifying the different beacons. We propose exploiting the conditional independence between the position distributions of each beacon within a Rao-Blackwellized Particle Filter (RBPF) for maintaining independent Sum of Gaussians (SOGs) for each beacon. Unlike other approaches, it is shown then that a proper probabilistic observation model can be derived for online operation with no need for delayed initializations. We provide a rigorous statistical comparison of this proposal with previous work of the authors where a Monte-Carlo approximation was employed instead for the conditional densities. As verified experimentally, this new proposal represents a significant improvement in accuracy, computation time, and robustness against outliers.

[1]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[3]  Javier González,et al.  A pure probabilistic approach to range-only SLAM , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[5]  Cipriano Galindo,et al.  Application of UWB and GPS technologies for vehicle localization in combined indoor-outdoor environments , 2007, 2007 9th International Symposium on Signal Processing and Its Applications.

[6]  Jun S. Liu,et al.  Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..

[7]  Edwin Olson,et al.  Robust Range-Only Beacon Localization , 2006 .

[8]  Gamini Dissanayake,et al.  An efficient multiple hypothesis filter for bearing-only SLAM , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[9]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[10]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[11]  Sanjiv Singh,et al.  Experimental results in range-only localization with radio , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[13]  Sanjiv Singh,et al.  Preliminary results in range-only localization and mapping , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[14]  Javier González,et al.  An optimal filtering algorithm for non-parametric observation models in robot localization , 2008, 2008 IEEE International Conference on Robotics and Automation.

[15]  Dieter Fox,et al.  Adapting the Sample Size in Particle Filters Through KLD-Sampling , 2003, Int. J. Robotics Res..

[16]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[17]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[18]  Sanjiv Singh,et al.  Recent Results in Extensions to Simultaneous Localization and Mapping , 2002, ISER.

[19]  Benjamin Grocholsky,et al.  Decentralized mapping of robot-aided sensor networks , 2008, 2008 IEEE International Conference on Robotics and Automation.

[20]  Sanjiv Singh,et al.  Range-only SLAM for robots operating cooperatively with sensor networks , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[21]  H. Durrant-Whyte,et al.  Simultaneous Localisation and Mapping ( SLAM ) : Part II State of the Art , 2006 .

[22]  Sebastian Thrun,et al.  Robotic mapping: a survey , 2003 .

[23]  John J. Leonard,et al.  Pure range-only sub-sea SLAM , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).