Uncertain map making in natural environments

Building on previous work on incremental natural scene modelling for mobile robot navigation, we focus in this paper on the problem of representing and managing uncertainties. The environment is composed of ground regions and objects. Objects (e.g., rocks) are represented by an uncertain state vector (location) and a variance-covariance matrix. Their shapes are approximated by ellipsoids. Landmarks are defined as objects with specific properties (discrimination, accuracy) that permit to use them for robot localization and for anchoring the environment model. Model updating is based on an extended Kalman filter. Experimental results are given that show the construction of a consistent model over tens of meters.

[1]  Michel Devy,et al.  Object-based modelling and localization in natural environments , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[2]  Ruzena Bajcsy,et al.  Volumetric segmentation of range images of 3D objects using superquadric models , 1993 .

[3]  Raja Chatila,et al.  Stochastic multisensory data fusion for mobile robot location and environment modeling , 1989 .

[4]  William B. Thompson,et al.  Localizing in unstructured environments: dealing with the errors , 1994, IEEE Trans. Robotics Autom..

[5]  S. Betge-Brezetz,et al.  Adaptive localization of an autonomous mobile robot in natural environments , 1994, Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems.

[6]  Katsushi Ikeuchi,et al.  A Spherical Representation for Recognition of Free-Form Surfaces , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Thierry Siméon,et al.  Autonomous navigation in outdoor environment: adaptive approach and experiment , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[8]  Alex Pentland,et al.  Perceptual Organization and the Representation of Natural Form , 1986, Artif. Intell..

[9]  Peter C. Cheeseman,et al.  Estimating uncertain spatial relationships in robotics , 1986, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[10]  Dmitry B. Goldgof,et al.  A Curvature-Based Approach to Terrain Recognition , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Patrick Hébert,et al.  Probabilistic Map Learning: Necessity and Difficulties , 1995, Reasoning with Uncertainty in Robotics.

[12]  S. Betge-Brezetz,et al.  Decoupling odometry and exteroceptive perception in building a global world map of a mobile robot: the use of local maps , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[13]  John J. Leonard,et al.  Directed Sonar Sensing for Mobile Robot Navigation , 1992 .