Drawing stereo disparity images into occupancy grids: Measurement model and fast implementation

Mapping the environment is necessary for navigation in unknown areas with autonomous vehicles. In this context, a method to process depth images for occupancy grid mapping is developed. Input data are images with pixel-based distance information and the corresponding camera poses. A measurement model, focusing on stereo-based depth images and their characteristics, is presented. Since an enormous amount of range data must be processed, improvements like image pyramids are used so that the image analysis is possible in real-time. Output is a grid-based image interpretation for sensor fusion, i.e. a world-centric occupancy probability array containing information stored in a single image. Different approaches to draw pixel information into a grid map are presented and discussed in terms of accuracy and performance. As a final result, 3D occupancy grids from aerial image sequences are presented.