Maximum likelihood rover localization by matching range maps

This paper describes maximum likelihood estimation techniques for performing rover localization in natural terrain by matching range maps. An occupancy map of the local terrain is first generated using stereo vision. The position of the rover with respect to a previously generated occupancy map is then computed by comparing the maps using a probabilistic formulation of image matching techniques. Our motivation for this work is the desire for greater autonomy in Mars rovers. These techniques have been applied to data obtained from the Sojourner Mars rover and run on-board the Rocky 7 Mars rover prototype.

[1]  Clark F. Olson Mobile robot self-localization by iconic matching of range maps , 1997, 1997 8th International Conference on Advanced Robotics. Proceedings. ICAR'97.

[2]  Fabio Gagliardi Cozman,et al.  Automatic mountain detection and pose estimation for teleoperation of lunar rovers , 1997, Proceedings of International Conference on Robotics and Automation.

[3]  Jake K. Aggarwal,et al.  Position estimation Techniques for an Autonomous Mobile robot - a Review , 1993, Handbook of Pattern Recognition and Computer Vision.

[4]  Alonzo Kelly,et al.  Obstacle detection for unmanned ground vehicles: a progress report , 1995 .

[5]  Robert Ivlev,et al.  The Rocky 7 rover: a Mars sciencecraft prototype , 1997, Proceedings of International Conference on Robotics and Automation.

[6]  Clark F. Olson,et al.  Visual Localization Methods for Mars Rovers Using Lander, Rover, and Descent Imagery , 1997 .

[7]  Daniel P. Huttenlocher,et al.  Comparing Images Using the Hausdorff Distance , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Eric Krotkov,et al.  Automatic Mountain Detection and Pose Estimation for Teleoperation of Lunar Rovers , 1997, ISER.

[9]  Clark F. Olson,et al.  Automatic target recognition by matching oriented edge pixels , 1997, IEEE Trans. Image Process..

[10]  J. Matijevic,et al.  Mars Pathfinder Microrover , 1995, Auton. Robots.

[11]  Clark F. Olson,et al.  A probabilistic formulation for Hausdorff matching , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[12]  William Rucklidge,et al.  Efficient Visual Recognition Using the Hausdorff Distance , 1996, Lecture Notes in Computer Science.

[13]  C. Olson A Probabilistic Formulation for Hausdorr Matching , 1998 .

[14]  C. Olson A Probabilistic Formulation for Hausdor Matching , .