| | |
Summary: 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
uncertainty 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.
I. INTRODUCTION
HERE are two types of sensor problems associated with
|
