Appearance-based topological Bayesian inference for loop-closing detection in cross-country environment

In this paper, an appearance-based environment modelling technique is presented. Based on this approach, the probabilistic Bayesian inference can work together with symbolic topological map to re-localize a mobile robot. One prominent advantage offered by this algorithm is that, it can be applied to cross-country environment where no features or landmarks are available. Furthermore, the loop-closing can be detected independent of estimated map and vehicle location. High dimensional laser measurements are projected into a low dimensional space (mapspace) which describes the appearance of the environment. Since laser scans from the same region share the similar appearance, after the projection, they are expected to form a distinct cluster in the low dimensional space. This small cluster essentially encodes the appearance information of the specific region in the environment, and it can be approximated by a Gaussian distribution. This Gaussian model can serve as the ´joint´ between the topological map structure and the probabilistic Bayesian inference. By employing such ´joints´, the Bayesian inference in the metric level can be conveniently implemented on topological level. Based on appearance, the proposed inference process is thus completely independent of local metric features. Extensive experiments were conducted using a tracked vehicle travelling in an open jungle environments. Results from live runs verified the feasibility of the proposed methods to detect loop-closing. The performances are also given and thoroughly analyzed.