Simultaneous Localization and Mapping for Non-parametric Potential Field Environments

This paper introduces a new method of simultaneous object tracking (localization) and environment mapping for objects moving in a potential field environment. Only weak non-parametric assumptions are made about the shape of the potential function using a Gaussian process prior. A second-and-a-half order numerical scheme for object motion in a potential field is formulated and it is shown how to use this for potential inference. The method improves tracking performance in structured environments, as is illustrated by its application to urban car tracking. Hidden environmental structure such as the location of obstructions can also be revealed. Prior knowledge (e.g. from maps) can easily be incorporated and can then be updated using feedback from tracking. Information from multiple targets can also be handled in a straightforward manner.