Continuous localization using evidence grids

Evidence grids provide a uniform representation for fusing temporally and spatially distinct sensor readings. However, the use of evidence grids requires that the robot be localized within its environment. Odometry errors typically accumulate over time, making localization estimates degrade, and introducing significant errors into evidence grids as they are built. We have addressed this problem by developing a method for "continuous localization", in which the robot corrects its localization estimates incrementally and on the fly. Assuming the mobile robot has a map of its environment represented as an evidence grid, localization is achieved by building a series of "local perception grids" based on localized sensor readings and the current odometry, and then registering the local and global grids. The registration produces an offset which is used to correct the odometry. Results are given on the effectiveness of this method, and quantify the improvement of continuous localization over dead reckoning. We also compare different techniques for matching evidence grids and for searching registration offsets.

[1]  Evangelos E. Milios,et al.  Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Alan C. Schultz,et al.  Mobile robot exploration and map-building with continuous localization , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[3]  Brian Yamauchi,et al.  Mobile robot localization in dynamic environments using dead reckoning and evidence grids , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[4]  James L. Crowley,et al.  Position estimation for a mobile robot using vision and odometry , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[5]  Rudolf Bauer.  Active manoeuvres for supporting the localisation process of an autonomous mobile robot , 1995, Robotics Auton. Syst..

[6]  Günther Schmidt,et al.  Continuous localization of a mobile robot based on 3D-laser-range-data, predicted sensor images, and dead-reckoning , 1995, Robotics Auton. Syst..

[7]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1992 .

[8]  Bernt Schiele,et al.  A comparison of position estimation techniques using occupancy grids , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[9]  Robin R. Murphy,et al.  Ultrasonic robot localization using Dempster-Shafer theory , 1992, Optics & Photonics.

[10]  Alan C. Schultz,et al.  Continuous localization in changing environments , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.