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Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

1
Temasek Laboratories, National University of Singapore, 117411 Singapore
2
College of Computer Science, Sichuan University, Chengdu 610065, China
3
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
4
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1449; https://doi.org/10.3390/s18051449
Received: 16 March 2018 / Revised: 28 April 2018 / Accepted: 5 May 2018 / Published: 6 May 2018
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
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Abstract

Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency. View Full-Text
Keywords: LiDAR; range data denoising; sparse coding; ridge constraint LiDAR; range data denoising; sparse coding; ridge constraint
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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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Gao, Z.; Lao, M.; Sang, Y.; Wen, F.; Ramesh, B.; Zhai, R. Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint. Sensors 2018, 18, 1449.

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