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Map-Based Navigation of an Autonomous Car Using Grid-Based Scan-to-Map Matching

[+] Author Affiliations
Tomonari Furukawa, Kuya Takami

Virginia Tech, Blacksburg, VA

Xianqiao Tong

NVIDIA Corporation, Santa Clara, CA

Daniel Watman, Abbi Hamed

ZMP, Inc., Tokyo, Japan

Ravindra Ranasinghe, Gamini Dissanayake

University of Technology, Sydney, NSW, Australia

Paper No. DETC2015-47936, pp. V003T01A005; 9 pages
doi:10.1115/DETC2015-47936
From:
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 17th International Conference on Advanced Vehicle Technologies; 12th International Conference on Design Education; 8th Frontiers in Biomedical Devices
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5710-6
  • Copyright © 2015 by ASME

abstract

This paper presents the map-based navigation of a car with autonomous capabilities using grid-based scan-to-map matching. The autonomous car used for demonstration is built based on Toyota Prius and can control the throttle, the brake and the steering by a computer. The proposed grid-based scan-to-map matching method represents a map with a finite number of grid cells, represents a scan and the map with scan points at each grid as normal distributions (NDs) and constructs a map by matching the scan NDs to the map NDs. The proposed method enables scan-based mapping at high speed while maintaining high accuracy. The representation of a grid cell of a map in terms of multiple NDs further enhances speed and accuracy. The accuracy analysis of the proposed method shows that a small robot with a wheel diameter of 8cm had yielded no loop closure error after the travel of 186m while the terminal position error by the GMapping was approximately 1m with the error growth of 1%. The application of the proposed method with the autonomous car has then demonstrated the ability of the proposed method for autonomous driving with varying and high speed and has also quantified the significance of speed for successful mapping in autonomous driving.

Copyright © 2015 by ASME

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