Environment Modeling For Visual Navigation

Visual navigation of a mobile robot in the natural environment is a difficult and comprehensive subject which is related to almost every aspects of computer vision researches. The fundamental tasks of visual navigation are composed of global localization, road following and obstacle detection. Environment modeling is the foundation of visual navigation. This dissertation is devoted to the deep and systematic study on the visual environment modeling. The main contributions include: 1. A task-oriented, multi-scale and full-view visual modeling strategy is proposed for the natural environment which combines the panoramic vision for scene modeling, omni-directional vision for road understanding and binocular vision for obstacle detection together. This approach overcomes the drawbacks of traditional visual navigation methods that mainly depended on local and/or single view visual information. In this direction sensor design, data processing and model representation are closely explored. 2. A two stage method is presented for the 3D panoramic scene modeling from vibrated image sequences which consists of (I) image stabilization by motion filtering and (2) depth estimation and depth boundary localization. The two stage method not only combines Zheng and Tsuji抯 panoramic image method with Bakers epipolar plane image analysis, resulting the so called panoramic epipolar plane image method, but also generalizes them to handle image sequence vibrations due to the un- controllable fluctuation of the camera. The two stage method by-passes the correspondence problem and ill-posed problem encountered in the general motion analysis, and avoids the local minimum problem of the spatial-constrain-based iteration method. 3. .A new road following approach . the Road Omni-View Image Neural Networks (ROVINN). is proposed which combines the omni-directional image sensing technique with neural networks. The ROVINN makes the robot never get lost and enables it to learn from the road images. The ROVINN approach brings Yagi抯 COPIS to the outdoor road scene and provides a new solution from the CMU抯 ALVINN. Compact and rotation-invariant image features are extracted by integrating the principle component analysis (PCA) and the Fourier transform (DFT). The modular neural networks can estimate road orientations more efficiently by first classif~抜ng the roads, and thus enable the robot to adapt to various road types automatically. 4. A novel method called image reprojection transformation is presented for road obstacle detection based on binocular vision. Dynamic reprojection transformation algorithms are developed so as to work in un-even road surface. The novelty of the (dynamic) reprojection transformation method, which ensembles the gaze control of the human vision, lies in the fact that it brings the road surface to zero disparity so that the feature extraction and matching procedures of the traditional stereo vision are avoided in the obstacle detection task. The progressive processing strategy of reproj ection transformation, yes/no verification and obstacle measurement make the obstacle detection efficient, fast and robust. 5. System implementations.(l) In the 3D panoramic scene modeling , the algorithms of motion filtering and image stabilization, kinetic occlusion detection and depth layering, have been developed so as to found a ground base for landmark selection of global localization and image synthesis of virtualized rea...