Sensor Selection Using Information Complexity for Multi-sensor Mobile Robot Localization

Abstract

Our sensor selection algorithm targets the problem of global self-localization of multi-sensor mobile robots. The algorithm builds on the probabilistic reasoning using Bayes filters to estimate sensor measurement uncertainty and sensor validity in robot localization. For quantifying measurement uncertainty we score the Bayesian belief probability density using a model selection criterion, and for sensor validity, we evaluate belief on pose estimates from different sensors as a multi-sample clustering problem. The minimization of the combined uncertainty (measurement uncertainly score + sensor validity score) allows us to intelligently choose a subset of sensors that contribute to accurate localization of the mobile robot. We demonstrate the capability of our sensor selection algorithm in automatically switching pose recovery methods and ignoring non-functional sensors for localization on real-world mobile platforms equipped with laser scanners, vision cameras, and other hardware instrumentation for pose estimation.