Building a dense surface map incrementally from semi-dense point cloud and RGBimages

Publication Type:
Journal Article
Citation:
Frontiers of Information Technology and Electronic Engineering, 2015, 16 (7), pp. 594 - 606
Issue Date:
2015-01-01
Full metadata record
Files in This Item:
Filename Description Size
Qianshan_ZUSA_clean.pdfAccepted Manuscript Version11.77 MB
Adobe PDF
© 2015, Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg. 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.
Please use this identifier to cite or link to this item: