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A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm

Department of Aeronautics and Astronautics, National Cheng Kung University, Tainan 701, Taiwan
Division of Human Mechanical Systems and Design, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, Japan
Lab of Smart Systems Engineering, Kitami Institute of Technology, Hokkaido, Kitami 090-8507, Japan
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1294;
Received: 17 March 2018 / Revised: 17 April 2018 / Accepted: 18 April 2018 / Published: 23 April 2018
(This article belongs to the Special Issue Selected Sensor Related Papers from ICI2017)
In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision. View Full-Text
Keywords: indoor localization; pose estimation; iterative closet point; SLAM; LiDAR indoor localization; pose estimation; iterative closet point; SLAM; LiDAR
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MDPI and ACS Style

Wang, Y.-T.; Peng, C.-C.; Ravankar, A.A.; Ravankar, A. A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm. Sensors 2018, 18, 1294.

AMA Style

Wang Y-T, Peng C-C, Ravankar AA, Ravankar A. A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm. Sensors. 2018; 18(4):1294.

Chicago/Turabian Style

Wang, Yun-Ting, Chao-Chung Peng, Ankit A. Ravankar, and Abhijeet Ravankar. 2018. "A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm" Sensors 18, no. 4: 1294.

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