Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
Probabilistic voxel mapping using an adaptive confidence measure of stereo matching
Intelligent Service Robotics
Object search using object co-occurrence relations derived from web content mining
Intelligent Service Robotics
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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.