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...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. Noine 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.展开更多
基金Project supported by the National Natural Science Foundation of China （Nos. 61075078 and 61473258）
文摘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. Noine 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.
