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Square Root Unscented Particle Filtering for Grid Mapping

Square Root Unscented Particle Filtering for Grid Mapping,10.1007/978-3-642-10439-8_13,Simone Zandara,Ann Nicholson

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Square Root Unscented Particle Filtering for Grid Mapping  
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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.
Conference: Australian Joint Conference on Artificial Intelligence - AUS-AI , pp. 121-130, 2009
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