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Open AccessArticle

A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving

1
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
2
Beijing Momenta Technology Company Limited, Beijing 100190, China
3
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1348; https://doi.org/10.3390/rs11111348
Received: 28 April 2019 / Revised: 2 June 2019 / Accepted: 3 June 2019 / Published: 4 June 2019
(This article belongs to the Special Issue Mobile Mapping Technologies)
Real-time and high-precision localization information is vital for many modules of unmanned vehicles. At present, a high-cost RTK (Real Time Kinematic) and IMU (Integrated Measurement Unit) integrated navigation system is often used, but its accuracy cannot meet the requirements and even fails in many scenes. In order to reduce the costs and improve the localization accuracy and stability, we propose a precise and robust segmentation-based Lidar (Light Detection and Ranging) localization system aided with MEMS (Micro-Electro-Mechanical System) IMU and designed for high level autonomous driving. Firstly, we extracted features from the online frame using a series of proposed efficient low-level semantic segmentation-based multiple types feature extraction algorithms, including ground, road-curb, edge, and surface. Next, we matched the adjacent frames in Lidar odometry module and matched the current frame with the dynamically loaded pre-build feature point cloud map in Lidar localization module based on the extracted features to precisely estimate the 6DoF (Degree of Freedom) pose, through the proposed priori information considered category matching algorithm and multi-group-step L-M (Levenberg-Marquardt) optimization algorithm. Finally, the lidar localization results were fused with MEMS IMU data through a state-error Kalman filter to produce smoother and more accurate localization information at a high frequency of 200Hz. The proposed localization system can achieve 3~5 cm in position and 0.05~0.1° in orientation RMS (Root Mean Square) accuracy and outperform previous state-of-the-art systems. The robustness and adaptability have been verified with localization testing data more than 1000 Km in various challenging scenes, including congested urban roads, narrow tunnels, textureless highways, and rain-like harsh weather. View Full-Text
Keywords: Lidar localization system; unmanned vehicle; segmentation-based feature extraction; category matching; multi-group-step L-M optimization; map management Lidar localization system; unmanned vehicle; segmentation-based feature extraction; category matching; multi-group-step L-M optimization; map management
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MDPI and ACS Style

Liu, H.; Ye, Q.; Wang, H.; Chen, L.; Yang, J. A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving. Remote Sens. 2019, 11, 1348. https://doi.org/10.3390/rs11111348

AMA Style

Liu H, Ye Q, Wang H, Chen L, Yang J. A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving. Remote Sensing. 2019; 11(11):1348. https://doi.org/10.3390/rs11111348

Chicago/Turabian Style

Liu, Hang; Ye, Qin; Wang, Hairui; Chen, Liang; Yang, Jian. 2019. "A Precise and Robust Segmentation-Based Lidar Localization System for Automated Urban Driving" Remote Sens. 11, no. 11: 1348. https://doi.org/10.3390/rs11111348

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