Stable exploration for bearings-only SLAM

Recent work on robotic exploration and active sensing has examined a variety of information-theoretic approaches to efficient and convergent map construction. These involve moving an exploring robot to locations in the world where the anticipated information gain is maximized. In this paper we demonstrate that, for map construction using bearings-only information and the Extended Kalman Filter (EKF), driving exploration so as to maximize expected information gain leads to ill-conditioned filter updates and a high probability of divergence between the inferred map and reality. In particular, we present analytical and numerical results demonstrating the effects of blindly applying an information-theoretic approach to bearings-only exploration. Subsequently, we present experimental results demonstrating that an exploration approach that favours the conditioning of the filter update will lead to more accurate maps.