Full Text:   <2226>

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CLC number: TP242.6

On-line Access: 2015-07-06

Received: 2014-09-03

Revision Accepted: 2015-01-24

Crosschecked: 2015-06-09

Cited: 0

Clicked: 6201

Citations:  Bibtex RefMan EndNote GB/T7714


Qian-shan Li


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Frontiers of Information Technology & Electronic Engineering  2015 Vol.16 No.7 P.594-606


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

Author(s):  Qian-shan Li, Rong Xiong, Shoudong Huang, Yi-ming Huang

Affiliation(s):  State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   liqianshan@zju.edu.cn, rxiong@iipc.zju.edu.cn, shoudong.huang@uts.edu.au, ymhuang@zju.edu.cn

Key Words:  Bionic robot, Robotic mapping, Surface fusion

Qian-shan Li, Rong Xiong, Shoudong Huang, Yi-ming Huang. Building a dense surface map incrementally from semi-dense point cloud and RGB images[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 594-606.

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A1 - Qian-shan Li
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DOI - 10.1631/FITEE.14a0260

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.

This paper introduces a method that increamentally constructs dense surface map using low cost devices. Their method has two steps: denoise and resample each scan, then incrementally fuse these scans. The research idea in the manuscript is interesting and the paper is well organized and is pleasing to read.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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