Efficient optimization of information-theoretic exploration in SLAM

Thomas Kollar, Nicholas Roy

We present a novel method for information-theoretic exploration, leveraging recent work on mapping and localization. We describe exploration as the constrained optimization problem of computing a trajectory to minimize posterior map error, subject to the constraints of traveling through a set of sensing locations to ensure map coverage. This trajectory is found by reducing the map to a skeleton graph and searching for a minimum entropy tour through the graph. We describe how a specific factorization of the map covariance allows the re-use of EKF updates during the optimization, giving an efficient gradient ascent search for the maximum information gain tour through sensing locations, where each tour naturally incorporates revisiting well-known map regions. By generating incrementally larger tours as the exploration finds new regions of the environment, we demonstrate that our approach can perform autonomous exploration with improved accuracy.

Subjects: 17. Robotics; 15.7 Search

Submitted: Apr 15, 2008

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