Model-Based Visual Self-localization Using Gaussian Spheres

Abstract

A novel model-based approach for global self-localization using active stereo vision and density Gaussian spheres is presented. The proposed object recognition components deliver noisy percept subgraphs, which are filtered and fused into an ego-centered reference frame. In subsequent stages, the required vision-to-model associations are extracted by selecting ego-percept subsets in order to prune and match the corresponding world-model subgraph. Ideally, these coupled subgraphs hold necessary information to obtain the model-to-world transformation, i.e., the pose of the robot. However, the estimation of the pose is not robust due to the uncertainties introduced when recovering Euclidean metric from images and during the mapping from the camera to the ego-center. The approach models the uncertainty of the percepts with a radial normal distribution. This formulation allows a closed-form solution which not only derives the maximal density position depicting the optimal ego-center but also ensures the solution even in situations where pure geometric spheres might not intersect.