Building a dense surface map incrementally from semi-dense point cloud andRGBimages
1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China.2. Faculty of Engineering and Information Technology, The University of Technology, Sydney, NSW 2007, Australia.3. ZJU-UTS Joint Center on Robotics, Zhejiang University, Hangzhou 310027, China.4. Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
发布日期：
2015-07-20
摘要
Building and using maps is a fundamental issue for bionic robots in field applications. A dense surface map, which offers rich visual and geometric information, is an ideal representation of the environment for indoor/outdoor localization, navigation, and recognition tasks of these robots. Since most bionic robots can use only small light-weight laser scanners and cameras to acquire semi-dense point cloud and RGB images, we propose a method to generate a consistent and dense surface map from this kind of semi-dense point cloud and RGB images. The method contains two main steps: (1) generate a dense surface for every single scan of point cloud and its corresponding image(s) and (2) incrementally fuse the dense surface of a new scan into the whole map. In step (1) edge-aware resampling is realized by segmenting the scan of a point cloud in advance and resampling each sub-cloud separately. Noise within the scan is reduced and a dense surface is generated. In step (2) the average surface is estimated probabilistically and the non-coincidence of different scans is eliminated. Experiments demonstrate that our method works well in both indoor and outdoor semi-structured environments where there are regularly shaped objects.
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