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JRM Vol.34 No.4 pp. 877-886
doi: 10.20965/jrm.2022.p0877
(2022)

Paper:

Localization Method Using Camera and LiDAR and its Application to Autonomous Mowing in Orchards

Hiroki Kurita*, Masaki Oku*, Takeshi Nakamura**, Takeshi Yoshida*, and Takanori Fukao*

*Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan

**OREC Co., Ltd.
548-22 Hiyoshi, Hirokawa-cho, Yame-gun, Fukuoka 834-0195, Japan

Received:
July 25, 2020
Accepted:
June 1, 2022
Published:
August 20, 2022
Keywords:
deep learning, LiDAR, sensor fusion, UGV, autonomous mowing
Abstract
Localization Method Using Camera and LiDAR and its Application to Autonomous Mowing in Orchards

Identified pear trunks

This paper presents a localization method using deep learning and light detection and ranging (LiDAR) for unmanned ground vehicle (UGV) in field environment. We develop a sensor fusion algorithm that UGV recognizes natural objects from RGB camera using deep learning and measures the distance to the recognized objects with LiDAR. UGV calculates its position relative to the objects, creates a reference path, and then executes path following control. The method is applied to autonomous mowing operation in orchard. A mower is tracked by UGV. UGV needs to run along a tree row keeping an appropriate distance from tree trunks, by which the mowing arm of the tracked mower properly touches the trunks. Field experiments are conducted in pear and apple orchards. UGV localizes self position relative to trees and performs autonomous mowing successfully. The results show that the presented method is effective.

Cite this article as:
H. Kurita, M. Oku, T. Nakamura, T. Yoshida, and T. Fukao, “Localization Method Using Camera and LiDAR and its Application to Autonomous Mowing in Orchards,” J. Robot. Mechatron., Vol.34, No.4, pp. 877-886, 2022.
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Last updated on Sep. 22, 2022