Photorealistic Scene Reconstruction by Voxel Coloring
International Journal of Computer Vision
A compact algorithm for rectification of stereo pairs
Machine Vision and Applications
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Towards Urban 3D Reconstruction from Video
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
International Journal of Computer Vision
3D Urban Scene Modeling Integrating Recognition and Reconstruction
International Journal of Computer Vision
Drawing stereo disparity images into occupancy grids: measurement model and fast implementation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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In this paper, we describe a probabilistic voxel mapping algorithm using an adaptive confidence measure of stereo matching. Most of the 3D mapping algorithms based on stereo matching usually generate a map formed by point cloud. There are many reconstruction errors. The reconstruction errors are due to stereo reconstruction error factors such as calibration errors, stereo matching errors, and triangulation errors. A point cloud map with reconstruction errors cannot accurately represent structures of environments and needs large memory capacity. To solve these problems, we focused on the confidence of stereo matching and probabilistic representation. For evaluation of stereo matching, we propose an adaptive confidence measure that is suitable for outdoor environments. The confidence of stereo matching can be reflected in the probability of restoring structures. For probabilistic representation, we propose a probabilistic voxel mapping algorithm. The proposed probabilistic voxel map is a more reliable representation of environments than the commonly used voxel map that just contains the occupancy information. We test the proposed confidence measure and probabilistic voxel mapping algorithm in outdoor environments.