Stabilizing information-driven exploration for bearings-only SLAM using range gating

This paper examines the problem of information-driven exploration for the purposes of simultaneous localization and mapping (SLAM) with a bearings-only sensor. In another work, we have demonstrated that employing an information-driven approach to exploration with an extended Kalman filter (EKF) can drive the robot to locations in the world where filter updates are ill-conditioned and linearization constraints are violated, potentially destabilizing the filter, and increasing the probability of divergence from the true state estimate. In this paper, we demonstrate an information-driven approach to exploration that preserves the stability of the EKF and produces maps that are significantly more accurate than a conventional information-driven approach. Our method is based on range-gating observations so as to avoid potentially destabilizing updates. We provide simulated experimental results demonstrating the superior performance of our approach over simple outlier gating and over heuristic-driven exploration.