Fast Circular Landmark Detection for Cooperative Localisation and Mapping

Map building through cooperative localisation (co-location) using circular geometric targets and a SICK laser range scanner is investigated. The tenet of co-location is circle detection in laser range data. Two methods for circle detection, a Range Weighted Circular Hough Transform (RWCHT) and a novel squared-residual voting strategy are compared and their performance assessed. The custom squared-residual voting strategy outperforms the RWCHT in all respects and is subsequently used for localisation and map building. The results include robust continuous localisation at speeds of 0.2m/s with 98% of scan frames used and an error of less than 0.03m. This localisation accuracy helps build maps of 96% quality and occupancy grids of cluttered environments despite the presence of distractors.