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Sensors 2016, 16(8), 1265; doi:10.3390/s16081265

Feature-Based Laser Scan Matching and Its Application for Indoor Mapping

1
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3
State Key Laboratory of Information Engineering, Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 12 May 2016 / Revised: 2 July 2016 / Accepted: 5 August 2016 / Published: 10 August 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [6975 KB, uploaded 10 August 2016]   |  

Abstract

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 < q < 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping. View Full-Text
Keywords: scan matching; point and line features; indoor mapping; relative pose estimation; lq-norm scan matching; point and line features; indoor mapping; relative pose estimation; lq-norm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, J.; Zhong, R.; Hu, Q.; Ai, M. Feature-Based Laser Scan Matching and Its Application for Indoor Mapping. Sensors 2016, 16, 1265.

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