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JRM Vol.32 No.3 pp. 624-633
doi: 10.20965/jrm.2020.p0624
(2020)

Development Report:

Mono-Camera-Based Robust Self-Localization Using LIDAR Intensity Map

Kei Sato*,**, Keisuke Yoneda**, Ryo Yanase**, and Naoki Suganuma**

*DENSO CORPORATION
1-1 Showa-cho, Kariya-shi, Aichi 448-8661, Japan

**Kanazawa University
Kakuma-machi, Kanazawa, Ishikawa 920-1192, Japan

Received:
January 10, 2020
Accepted:
April 24, 2020
Published:
June 20, 2020
Keywords:
automated vehicle, image processing, self-localization
Abstract
Mono-Camera-Based Robust Self-Localization Using LIDAR Intensity Map

SSD score distribution obtained by template matching using the proposed method

An image-based self-localization method for automated vehicles is proposed herein. The general self-localization method estimates a vehicle’s location on a map by collating a predefined map with a sensor’s observation values. The same sensor, generally light detection and ranging (LIDAR), is used to acquire map data and observation values. In this study, to develop a low-cost self-localization system, we estimate the vehicle’s location on a LIDAR-created map using images captured by a mono-camera. The similarity distribution between a mono-camera image transformed into a bird’s-eye image and a map is created in advance by template matching the images. Furthermore, a method to estimate a vehicle’s location based on the acquired similarity is proposed. The proposed self-localization method is evaluated on the driving data from urban public roads; it is found that the proposed method improved the robustness of the self-localization system compared with the previous camera-based method.

Cite this article as:
K. Sato, K. Yoneda, R. Yanase, and N. Suganuma, “Mono-Camera-Based Robust Self-Localization Using LIDAR Intensity Map,” J. Robot. Mechatron., Vol.32, No.3, pp. 624-633, 2020.
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Last updated on Jul. 04, 2020