Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/79548
Title: | Performance analysis of NDT-based graph SLAM for autonomous vehicle in diverse typical driving scenarios of Hong Kong | Authors: | Wen, W Hsu, LT Zhang, G |
Issue Date: | 2018 | Source: | Sensors (Switzerland), 2018, v. 18, no. 11, 3928, p. 1-21 | Abstract: | Robust and lane-level positioning is essential for autonomous vehicles. As an irreplaceable sensor, Light detection and ranging (LiDAR) can provide continuous and high-frequency pose estimation by means of mapping, on condition that enough environment features are available. The error of mapping can accumulate over time. Therefore, LiDAR is usually integrated with other sensors. In diverse urban scenarios, the environment feature availability relies heavily on the traffic (moving and static objects) and the degree of urbanization. Common LiDAR-based simultaneous localization and mapping (SLAM) demonstrations tend to be studied in light traffic and less urbanized area. However, its performance can be severely challenged in deep urbanized cities, such as Hong Kong, Tokyo, and New York with dense traffic and tall buildings. This paper proposes to analyze the performance of standalone NDT-based graph SLAM and its reliability estimation in diverse urban scenarios to further evaluate the relationship between the performance of LiDAR-based SLAM and scenario conditions. The normal distribution transform (NDT) is employed to calculate the transformation between frames of point clouds. Then, the LiDAR odometry is performed based on the calculated continuous transformation. The state-of-the-art graph-based optimization is used to integrate the LiDAR odometry measurements to implement optimization. The 3D building models are generated and the definition of the degree of urbanization based on Skyplot is proposed. Experiments are implemented in different scenarios with different degrees of urbanization and traffic conditions. The results show that the performance of the LiDAR-based SLAM using NDT is strongly related to the traffic condition and degree of urbanization. The best performance is achieved in the sparse area with normal traffic and the worse performance is obtained in dense urban area with 3D positioning error (summation of horizontal and vertical) gradients of 0.024 m/s and 0.189 m/s, respectively. The analyzed results can be a comprehensive benchmark for evaluating the performance of standalone NDT-based graph SLAM in diverse scenarios which is significant for multi-sensor fusion of autonomous vehicle. | Keywords: | Localization NDT Graph SLAM LiDAR Autonomous Vehicle |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Sensors (Switzerland) | EISSN: | 1424-8220 | DOI: | 10.3390/s18113928 | Rights: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The following publication Wen, W., Hsu, L. -., & Zhang, G. (2018). Performance analysis of NDT-based graph SLAM for autonomous vehicle in diverse typical driving scenarios of Hong Kong. Sensors (Basel, Switzerland), 18(11), 3928, 1-21 is available at https://doi.org/10.3390/s18113928 |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Wen_NDT-based_Graph_Slam.pdf | 2.77 MB | Adobe PDF | View/Open |
Page views
338
Last Week
0
0
Last month
Citations as of Apr 28, 2024
Downloads
202
Citations as of Apr 28, 2024
SCOPUSTM
Citations
52
Citations as of Apr 26, 2024
WEB OF SCIENCETM
Citations
47
Last Week
0
0
Last month
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.