Motion Constrained Simultaneous Localization and Mapping in Neighbourhood Environments

The ability to construct a consistent map whilst determining its pose is a prerequisite for mobile robot navigation. When operating in circuit-like trajectories (e.g. surveillance), acquiring large cyclic maps and closing these trajectories is very important. This paper presents the use of map-based a-priori knowledge for constraining the solution to the simultaneous localization and mapping problem. This is formulated in a Bayesian framework where a probabilistic model of the traversable environment (e.g. road network map) is used to minimise the accumulated errors in robot localization during cyclic mapping. The implementation uses an adaptive Rao-Blackwellized particle filter to optimise computation. The map-based priors are generated from a road network map that describes the robot traversable areas. The principle is to bind effectively the adaptive sample-based representation of the robot estimation using the probability map whilst detecting and mapping distinct features found at the roadsides. it allows the number of samples to be reduced and the particle depletion problem that compromises the robot closing large loops minimized. Extensive experiments were conducted using a vehicle test-bed travelling at 15Km/h in a University Campus road network. These have shown that our approach enables us to build cyclic maps and thus close the cycles (trajectories) in a much better manner than other existing methods.