Rao-Blackwellized Particle Filter SLAM with Prior Map: An Experimental Evaluation

Robotic applications demanding high-precision positioning, such as material handling by autonomous forklifts, require accurate and consistent maps of the environment. Moreover, exact correspondence between features of the architectural floor plan and the vehicle navigation map is desirable, as pick-up and drop-off locations are typically laid out on the floor plan. In this paper, we present a modification of the well-established grid-based Rao-Blackwellized particle filter (RBPF), which enables efficient initialization of its internal data structures using information from the architectural floor plan. This prior information simultaneously improves mapping accuracy and yields a map that has a high degree of correspondence with the floor plan. The proposed method is evaluated experimentally, on a publicly available localization dataset from the MIT Stata Centre, and on a dataset acquired with a mobile platform at the University of Zagreb.