Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization

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Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization
Title:
Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization
Journal Title:
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Keywords:
Publication Date:
03 November 2019
Citation:
S. Z. Ahmed, V. B. Saputra, S. Verma, K. Zhang and A. H. Adiwahono, "Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization," 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019, pp. 1614-1619. doi: 10.1109/IROS40897.2019.8967596
Abstract:
Difficulties in capturing unique structures in the outdoor environment hinders the map-based Autonomous Vehicles (AV) localization performance. Accordingly, this necessitates the use of high resolution sensors to capture more information from the environment. However, this approach is costly and limits the mass deployment of AV. To overcome this drawback, in this paper, we propose a novel outdoor map-based localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a Point-to-Distribution (P2D) formulation of the Normal Distributions Transform (NDT) approach is applied in a Monte Carlo Localization (MCL) framework. The formulation improves the measurement model of localization by taking individual lidar point measurements into consideration. Additionally, to apply the localization to scalable outdoor environments, a flexible and efficient map structure is implemented. The experimental results indicate that the proposed approach significantly improves the localization and its robustness in outdoor AV environments, especially with limited sparse lidar data.
License type:
PublisherCopyrights
Funding Info:
Description:
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2153-0866
2153-0858
ISBN:
978-1-7281-4004-9
978-1-7281-4003-2
978-1-7281-4005-6
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