Square Root Unscented Particle Filtering for Grid Mapping

Authors:
Simone Zandara;Ann Nicholson
Affiliations:
Monash University, Clayton 3800;Monash University, Clayton 3800
Venue:
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Year:
2009

Citing 3
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Abstract

In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [12]. Various filtering methods --- Particle Filtering, and derived Kalman Filter methods (Extended, Unscented) --- have been applied successfully to SLAM. We present a new algorithm that adapts the Square Root Unscented Transformation [13], previously only applied to feature based maps [5], to grid mapping. We also present a new method for the so-called pose-correction step in the algorithm. Experimental results show improved computational performance on more complex grid maps compared to an existing grid based particle filtering algorithm.