A Probabilistic Framework for Real-Time Mapping on an Unmanned Ground Vehicle

Abstract

In the course of preparing for the 2005 DARPA Grand Challenge, an off-road race for autonomous vehicles, a group of undergraduates from Caltech developed a set of deterministic algorithms for performing sensor fusion on maps generated by different range sensors on a mobile robot. That framework had serious limitations, however, including “disappearing” obstacles and lack of confidence data associated with features in the maps. In this thesis, we present a probabilistic framework that attempts to solve some of these problems by using error models of two typical types of range sensors, as well as by making use of Kalman filtering techniques from control theory to fuse the resulting measurements into an accurate digital elevation map. Our results indicate that this probabilistic framework has several advantages over the determinisic framework used by Team Caltech in the 2005 Grand Challenge.