Occupancy grid mapping: An empirical evaluation

In this paper a quantitative analysis of robotic mapping utilising the fields dominant paradigm, the occupancy grid, is presented. The aim of this work is to determine which approach to the robotic mapping problem imbues a mobile robot with the greatest ability to create an accurate representation of its operating environment. We accomplish this by analysing the performance of several established mapping techniques using identical test data. Through evaluating the maps generated by these paradigms using an extensible benchmarking suite that our group has developed we outline which paradigm yields the greatest representational ability.

[1]  David Lee.  The map-building and exploration strategies of a simple sonar-equipped mobile robot , 1996 .

[2]  Yoram Koren,et al.  The vector field histogram-fast obstacle avoidance for mobile robots , 1991, IEEE Trans. Robotics Autom..

[3]  Sebastian Thrun,et al.  Exploration and model building in mobile robot domains , 1993, IEEE International Conference on Neural Networks.

[4]  Hugh F. Durrant-Whyte,et al.  A solution to the simultaneous localization and map building (SLAM) problem , 2001, IEEE Trans. Robotics Autom..

[5]  Larry H. Matthies,et al.  Integration of sonar and stereo range data using a grid-based representation , 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  Shane O'Sullivan,et al.  Evaluating Techniques for Resolving Redundant Information and Specularity in Occupancy Grids , 2005, Australian Conference on Artificial Intelligence.

[8]  Sebastian Thrun,et al.  Learning occupancy grids with forward models , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[9]  Robert J. Baron,et al.  Mechanisms of Human Facial Recognition , 1981, Int. J. Man Mach. Stud..

[10]  Kurt Konolige,et al.  Improved Occupancy Grids for Map Building , 1997, Auton. Robots.

[11]  Hans P. Moravec,et al.  Robot Evidence Grids. , 1996 .

[12]  Alberto Elfes.  DYNAMIC CONTROL OF ROBOT PERCEPTION USING STOCHASTIC SPATIAL MODELS , 2005 .

[13]  Wolfram Burgard,et al.  A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots , 1998, Auton. Robots.

[14]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[15]  Alberto Elfes,et al.  Occupancy grids: a probabilistic framework for robot perception and navigation , 1989 .

[16]  Dong-Woo Cho.  Certainty Grid Representation for Robot Navigation by a Bayesian Method , 1990, Robotica.