A Statistical Approach to Goniometric Robot Location including Data Fusion and Error Rejection

For mobile robots to be able to work with and for people and thus operate in our everyday environments, they need to be able to acquire knowledge through perception. In other words they need to collect sensor measure­ ments from which they extract meaningful information. This thesis covers some of the essential components of a robot perception system combining omnidirectional vision, odometry, and 3D laser range finders, from modeling to extrinsic calibration, from feature extraction to ego-motion estimation. We covers all these topics from the “point of view” of an omnidirectional camera. The contributions of this work are several and are listed here. The thesis starts with an overview of the geometry of central omnidirec­ tional cameras and gives also an overview of previous calibration methods. The contributions of this section are three. The first two are a new generalized model for describing both dioptric and catadioptric cameras and a calibration method which takes advantage of planar grids shown around the cameras, like the method in use for standard perspective cameras. The third contribution is the implementation of a toolbox for Matlab (called OCamCalib and freely available on-line), which implements the proposed calibration procedure. The second part of the thesis is dedicated to the extraction and matching of vertical features from omnidirectional images. Vertical features are usu­ ally very predominant in indoor and outdoor structured environments and can then be very useful for robot navigation. The contribution of this part is a new method for matching vertical lines. The proposed method takes ad­ vantage of a descriptor that is very distinctive for each feature. Furthermore, this descriptor is invariant to rotation and slight changes of illumination. The third part of the thesis is devoted to the extrinsic calibration of an omnidirectional camera with the odometry (i.e. wheel enco ders) of a mobile robot. The contribution of this part is a new method of automatic self­