Visual features extraction based egomotion calculation from a infrared time-of-flight camera

Abstract

3D data have been used for robotics tasks in the last years. These data provide valuable information about the robot environment. Traditionally, stereo cameras has been used to obtain 3D data, but these kind of cameras do not provide information in the lack of texture. There is a new camera, SR4000, which uses infrared light in order to get richer information. In this paper we first analyze this camera. Then, we detail an efficient ICP-like method to build complete 3D models combining Growing Neural Gas (GNG) and visual features. First, we adapt the GNG to the 3D cloud points. Then, we propose the calculation of visual features and its registration to the elements of the GNG. Finally, we use correspondences between frames, an ICP-like method to calculate egomotion. Results of mapping from the egomotion are shown. © 2011 Springer-Verlag.