Building a dense surface map incrementally from semi-dense point cloud andRGBimages

doi:10.1631/FITEE.14a0260

Frontiers of Information Technology & Electronic Engineering

2015, Vol. 16

Issue (7): 594-606 https://doi.org/10.1631/FITEE.14a0260

本期目录

Building a dense surface map incrementally from semi-dense point cloud andRGBimages

Qian-shan LI^1,³(

),Rong XIONG^1,^3,^*(

),Shoudong HUANG^2,³(

),Yi-ming HUANG⁴(

)

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

全文: PDF(1900 KB)

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.

Key words： Bionic robot Robotic mapping Surface fusion

收稿日期: 2014-09-03 出版日期: 2015-07-20

Corresponding Author(s): Rong XIONG

引用本文:

. [J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 594-606.
Qian-shan LI,Rong XIONG,Shoudong HUANG,Yi-ming HUANG. Building a dense surface map incrementally from semi-dense point cloud andRGBimages. Front. Inform. Technol. Electron. Eng, 2015, 16(7): 594-606.

链接本文:

https://academic.hep.com.cn/fitee/CN/10.1631/FITEE.14a0260
https://academic.hep.com.cn/fitee/CN/Y2015/V16/I7/594

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