Efficient Optimization of Information-Theoretic Exploration in SLAM

Abstract

We present a novel method for information-theoretic ex-ploration, leveraging recent work on mapping and lo-calization. We describe exploration as the constrained optimization problem of computing a trajectory to mini-mize 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 spe-cific factorization of the map covariance allows the re-use of EKF updates during the optimization, giving an efficient gradient ascent search for the maximum infor-mation 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.