Range information in omnidirectional images through laser range finder

Robot map navigation and localization are challenges that, if they are to be overcome, require the solving of the data association problem for local and global features. Data fusion approaches allow highlight the advantages of two or more sensors, and complementary cooperation can be obtained. This paper presents two methods to embed depth information in omnidirectional images using the extrinsic calibration of a 2D laser range finder and a central catadioptric camera. The methods presented do not require a visible laser beam, but they assume the planar checkerboards patterns are visible for both the catadioptric camera and the 2D laser range finder. Unlike other approaches, the methods proposed are evaluated at pixel error level using ground truth data from the calibration patterns projected in the omnidirectional image. Results include a mean square error analysis of all calibration poses, and laser point projection on indoor omnidirectional images. We think that embedding range information in omnidirectional images is an interesting tool for data fusion approaches, which can be used in robot map building and localization. Keywords-Omnidirectional vision, extrinsic calibration, laser range finder, sensor fusion