Improved intelligent vehicle self-localization with integration of sparse visual map and high-speed pavement visual odometry. (January 2021)

• Record Type: Journal Article
• Authors: Huang, Gang; Hu, Zhaozheng; Tao, Qianwen; Zhang, Fan; Zhou, Zhe
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 235:Number 1(2021)
• Page Start: 177
• Page End: 187
• Publication Date: 2021-01
• Subjects: Intelligent vehicle -- vehicle localization -- visual odometry -- visual map -- high-speed camera; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407020943306
• Languages: English
• ISSNs: 0954-4070

Abstract

Localization is a fundamental requirement for intelligent vehicles. Conventional localization methods usually suffer from various limitations, such as low accuracy and blocked areas for Global Positioning System, high cost for inertial navigation system or light detection and ranging, and low robustness for visual simultaneous localization and mapping or visual odometry. To overcome these problems, we propose a novel localization method integrated with a sparse visual map and a high-speed pavement visual odometry. We use a lateral-view camera to sense the sparse visual map node for accurate map-based localization. We use a down-view high-speed camera for odometry computation between two sparse visual map nodes. With a high-speed camera, it is possible to extract and track pavement features with stable resolution imaging even in high-speed movement. We also develop a data-driven motion model for the Kalman filter to fuse the localization results from the sparse map and the high-speed pavement visual odometry to enhance vehicle localization. The proposed method was tested in two different scenarios in different pavement conditions. The experimental results demonstrate that the proposed method can improve vehicle localization with low cost and high feasibility.