JRM Vol.35 No.2 pp. 347-361
doi: 10.20965/jrm.2023.p0347


Turning at Intersections Using Virtual LiDAR Signals Obtained from a Segmentation Result

Miho Adachi* ORCID Icon, Kazufumi Honda*, and Ryusuke Miyamoto** ORCID Icon

*Department of Computer Science, Graduate School of Science and Technology, Meiji University
1-1-1 Higashimita Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan

**Department of Computer Science, School of Science and Technology, Meiji University
1-1-1 Higashimita Tama-ku, Kawasaki-shi, Kanagawa 214-8571, Japan

October 24, 2022
February 24, 2023
April 20, 2023
visual navigation, semantic segmentation, Virtual LiDAR, road following, obstacle avoidance

We implemented a novel visual navigation method for autonomous mobile robots, which is based on the results of semantic segmentation. The novelty of this method lies in its control strategy used for a robot during road-following: the robot moves toward a target point determined through semantic information. Previous implementations of the method sometimes failed to turn at an intersection owing to a fixed value of the turning angle. To address this issue, this study proposes a novel method for turning at an intersection using a control method based on a target point, which was originally developed for road-following. Here, an intersection is modeled as consisting of multiple straight roads. Evaluation using the CARLA simulator showed that the proposed method could accurately estimate the parameters representing a virtual road composing an intersection. In addition, run experiments conducted at the Ikuta Campus of Meiji University using an actual robot confirmed that the proposed method could appropriately make turns at intersections.

Road following on a virtual road

Road following on a virtual road

Cite this article as:
M. Adachi, K. Honda, and R. Miyamoto, “Turning at Intersections Using Virtual LiDAR Signals Obtained from a Segmentation Result,” J. Robot. Mechatron., Vol.35 No.2, pp. 347-361, 2023.
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Last updated on Apr. 22, 2024