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Performance Improvement of Iterative Closest Point-Based Outdoor SLAM by Rotation Invariant Descriptors of Salient Regions

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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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Authors and Affiliations

  1. School of Mechanical Engineering, Korea University, Seoul, Korea

    Yong-Ju Lee & Jae-Bok Song

  2. Agency for Defense Development, Daejeon, Korea

    Ji-Hoon Choi

Authors
  1. Yong-Ju Lee
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  2. Jae-Bok Song
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  3. Ji-Hoon Choi
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Correspondence to Jae-Bok Song.

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Lee, YJ., Song, JB. & Choi, JH. Performance Improvement of Iterative Closest Point-Based Outdoor SLAM by Rotation Invariant Descriptors of Salient Regions. J Intell Robot Syst 71, 349–360 (2013). https://doi.org/10.1007/s10846-012-9786-2

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  • DOI: https://doi.org/10.1007/s10846-012-9786-2

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