Embedding Range Information in Omnidirectional Images through Laser Range Finder

Robot map navigation and localization are challenging tasks that require the solving of the data association problem for local and global features. Data fusion allows the advantages of two or more sensors to be combined, 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 checkerboard patterns are visible for both the catadioptric camera and the 2D laser range finder. Unlike other approaches, the methods proposed used an invisible laser trace, and they 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.