Paper Info
Title
Building a dense surface map incrementally from semi-dense point cloud and RGBimages.
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.
Year
DOI
Venue
2015
10.1631/FITEE.14a0260
Frontiers of IT & EE
Keywords
DocType
Volume
Bionic robot, Robotic mapping, Surface fusion, TP242.6
Journal
16
Issue
ISSN
Citations 
7
2095-9230
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Qianshan Li100.34
Rong Xiong246.49
Shoudong Huang375562.77
Yi-Ming Huang400.68