Performance Improvement of Iterative Closest Point-Based Outdoor SLAM by Rotation Invariant Descriptors of Salient Regions
- Authors
- Lee, Yong-Ju; Song, Jae-Bok; Choi, Ji-Hoon
- Issue Date
- 9월-2013
- Publisher
- SPRINGER
- Keywords
- Mapping; SLAM; Iterative closest point (ICP); 3-D maps; Outdoor navigation
- Citation
- JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, v.71, no.3-4, pp.349 - 360
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
- Volume
- 71
- Number
- 3-4
- Start Page
- 349
- End Page
- 360
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/102235
- DOI
- 10.1007/s10846-012-9786-2
- ISSN
- 0921-0296
- Abstract
- To navigate in an unknown environment, a robot should build a model for the environment. For outdoor environments, a three-dimensional (3-D) map is usually used as a main model. This study considers outdoor simultaneous localization and mapping (SLAM) to build a global 3-D map by matching local 3-D maps. An iterative closest point (ICP) algorithm is used to match local 3-D maps and estimate a robot pose, but an alignment error is generated by the ICP algorithm due to the false selection of corresponding points. We propose a new method to extract 3-D points that are valid for ICP matching. Rotation-invariant descriptors are introduced for robust correspondence. 3-D environmental data acquired by tilting a 2-D laser scanner are used to build local 3-D maps. Experimental results in real environments show the increased accuracy of the ICP-based matching and a reduction in matching time.
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Collections - College of Engineering > Department of Mechanical Engineering > 1. Journal Articles
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