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

Jiayuan Li¹, Ruofei Zhong², Qingwu Hu³

¹School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China. ljy_whu_2012@whu.edu.cn.

Sensors (Basel, Switzerland)

|August 13, 2016

Summary

This study introduces an initial-free 2D laser scan matching method using point and line features, improving indoor positioning and mapping accuracy without relying on prior transformations.

Keywords:

indoor mapping lq-norm point and line features relative pose estimation scan matching

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Area of Science:

Robotics
Computer Vision
Geomatics

Background:

Scan matching is crucial for indoor positioning and modeling, but existing Iterative Closest Point (ICP) algorithms require accurate initial transformations.
The reliance on initial guesses limits the practical applicability of ICP algorithms in real-world scenarios.

Purpose of the Study:

To develop an initial-free 2D laser scan matching method that overcomes the limitations of traditional ICP algorithms.
To enhance the robustness and accuracy of scan matching for indoor positioning and mapping applications.

Main Methods:

Proposed an initial-free 2D laser scan matching method utilizing point and line features.
Implemented an extended 1D SIFT for feature point detection and a modified Split-and-Merge algorithm for line feature extraction.
Utilized a distance histogram and histogram clustering for feature correspondence and outlier filtering, providing an accurate initial rigid transformation.
Introduced a novel relative pose estimation using an lq-norm (0 < q < 1) metric for outlier robustness.

Main Results:

The proposed method achieves accuracy comparable to ICP algorithms but is initial-free.
Demonstrated effective feature correspondence detection and outlier rejection using histogram clustering.
The lq-norm based pose estimation proved robust against outliers.

Conclusions:

The developed initial-free scan matching method significantly advances indoor positioning and modeling capabilities.
The method's accuracy and robustness make it a viable alternative to ICP, especially when initial transformations are unavailable.
The scan matching technique can be successfully integrated into Simultaneous Localization and Mapping (SLAM) systems for enhanced indoor mapping.