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

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Abstract

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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Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Qian-shan Li & Rong Xiong

  2. Faculty of Engineering and Information Technology, The University of Technology, Sydney, NSW, 2007, Australia

    Shoudong Huang

  3. ZJU-UTS Joint Center on Robotics, Zhejiang University, Hangzhou, 310027, China

    Qian-shan Li, Rong Xiong & Shoudong Huang

  4. Department of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China

    Yi-ming Huang

Authors
  1. Qian-shan Li
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  2. Rong Xiong
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  3. Shoudong Huang
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  4. Yi-ming Huang
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Corresponding author

Correspondence to Rong Xiong.

Additional information

Project supported by the National Natural Science Foundation of China (Nos. 61075078 and 61473258)

ORCID: Qian-shan LI, http://orcid.org/0000-0003-0370-7100

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Li, Qs., Xiong, R., Huang, S. et al. Building a dense surface map incrementally from semi-dense point cloud and RGBimages. Frontiers Inf Technol Electronic Eng 16, 594–606 (2015). https://doi.org/10.1631/FITEE.14a0260

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  • DOI: https://doi.org/10.1631/FITEE.14a0260

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