| In this paper, an appearance-based
environment modeling technique is presented. Based on this approach, the
probabilistic Bayesian inference can work together with a symbolic topological
map to relocalize a mobile robot. One prominent advantage offered by this
algorithm is that it can be applied to a cross-country environment where
no features or landmarks are available. Further more, the loopclosing can
be detected independently of estimated map and vehicle location. High dimensional
laser measurements are projected into a low dimensional space (mapspace)
whichdescribes the appearance of the environment. Since laser scans from
the same region share a similar appearance, after the projection, they are
expected to form a distinct cluster in the low dimensional space. This small
cluster essentially encodes 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 a 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 traveling
in an open jungle environment. Results from live runs verified the feasibility
of using the proposed methods to detect loop-closing. The performances are
also given and thoroughly analyzed. |
