Multi-Observation Sensor Resetting Localization with Ambiguous Landmarks
Successful approaches to the robot localization problem include Monte Carlo particle filters, which estimate non-parametric localization belief distributions. However, particle filters fare poorly at determining the robot's position without a good initial hypothesis. This problem has been addressed for robots that sense visual landmarks with sensor resetting, by performing sensor-based resampling when the robot is lost. For robots that make sparse, ambiguous and noisy observations, standard sensor resetting places new location hypotheses across a wide region, in positions that may be inconsistent with previous observations. We propose Multi-Observation Sensor Resetting, where observations from multiple frames are merged to generate new hypotheses more effectively. We demonstrate experimentally in the robot soccer domain on the NAO humanoid robots that Multi-Observation Sensor Resetting converges more efficiently to the robot's true position than standard sensor resetting, and is more robust to systematic vision errors.