language: en

Integrating Topological and Metric Maps for Mobile Robot Navigation: A Statistical Approach

Sebastian Thrun 1 , Steffen Gutmann 2 , Dieter Fox 3 , Wolfram Burgard 3 , and Benjamin J. Kuipers 4
1 Computer Science Department 2 Institut fur Informatik 3 Institut fur Informatik III 4 Computer Science Department Carnegie Mellon University Universitat Freiburg University of Bonn University of Texas at Austin
Pittsburgh, PA 15213 D-79110 Freiburg, Germany D-53117 Bonn, Germany Austin, TX 78712

[submitted to AAAI-98]

Abstract The problem of concurrent mapping and localization has received considerable attention in the mobile robotics community. With few exceptions, existing approaches can largely be grouped into two distinct paradigms: topological and metric. This paper proposes a method that integrates both paradigms. It poses the mapping problem as a statistical maximum likelihood problem, and devises an efficient algorithm for search in likelihood space. Based on that, it presents an novel mapping algorithm that integrates two phases: a topological and a metric mapping phase. The topological mapping phase solves a global position alignment problem between potentially indistinguishable, significant places. The subsequent metric mapping phase produces a fine-grained metric map of the environment in floating-point resolution. Experimental results in cyclic environments of sizes up to 80 by 25 meters demonstrate the appropriateness of this approach.

Introduction