Development of an autonomous driving system using 3D-LiDAR and SLAM in an orchard

被引：0
|
作者：
Kaizu, Yutaka [1 ]
Okamoto, Ryosuke [1 ,2 ]
Igarashi, Sho [1 ]
Furuhashi, Kenichi [1 ]
Imou, Kenji [1 ]
机构：
[1] Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan
[2] Kubota Corp., Japan
来源：
Engineering in Agriculture, Environment and Food | 2024年 / 17卷 / 01期
关键词：
Agricultural robots - Automobile drivers - Autonomous vehicles - Fruits - Mapping - Normal distribution - Optical radar - Reusability;
D O I：
10.37221/eaef.17.1_1
中图分类号：
学科分类号：
摘要：
In orchards, there are many tasks that have not yet been automated, such as harvesting, transportation, weeding, and monitoring. In this study, we attempted 3D mapping and automatic driving in a chestnut orchard using simultaneous localization and mapping (SLAM) with 3D-LiDAR and an IMU. As a result, parameters suitable for normal distribution transform (NDT) matching in a chestnut orchard were identified. In automatic driving, the lateral error of the path was less than 10 cm, which was sufficient for an agricultural robot to travel in an orchard. The effect of the abundance of foliage on the accuracy of self-position estimation was small, indicating the reusability of the created 3D map. © 2024 Asian Agricultural and Biological Engineering Association. All rights reserved.
引用
收藏
页码：1 / 11
相关论文
共 50 条
  • [41] Mapping and localization for autonomous ship using LiDAR SLAM on the sea
    Ryohei Sawada
    Koichi Hirata
    Journal of Marine Science and Technology, 2023, 28 : 410 - 421
  • [42] Mapping and localization for autonomous ship using LiDAR SLAM on the sea
    Sawada, Ryohei
    Hirata, Koichi
    Journal of Marine Science and Technology (Japan), 2023, 28 (02): : 410 - 421
  • [43] Design and development of an integrated LiDAR sensor for autonomous driving
    Hofbauer, A.
    Reichert, R.
    Hennecke, M.
    Baumgart, M.
    Tortschanoff, A.
    Kirillov, B.
    Pfennigbauer, M.
    ELECTRO-OPTICAL AND INFRARED SYSTEMS: TECHNOLOGY AND APPLICATIONS XX, 2023, 12737
  • [44] Advancing autonomous vehicle safety assessment: A novel methodology for moving from functional to concrete scenarios using kinetic 3D-LiDAR and SHAP
    Kang, Minhee
    Eom, Saeyan
    Hwang, Keeyeon
    RESULTS IN ENGINEERING, 2024, 24
  • [45] Road-Segmentation-Based Curb Detection Method for Self-Driving via a 3D-LiDAR Sensor
    Zhang, Yihuan
    Wang, Jun
    Wang, Xiaonian
    Dolan, John M.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 19 (12) : 3981 - 3991
  • [46] Fast planar surface 3D SLAM using LIDAR
    Lenac, Kruno
    Kitanov, Andrej
    Cupec, Robert
    Petrovic, Ivan
    ROBOTICS AND AUTONOMOUS SYSTEMS, 2017, 92 : 197 - 220
  • [47] Real-time 3D-LiDAR object detection in autonomous vehicle systems using cluster-based candidates and deep learning
    Kim M.-G.
    Bae S.-H.
    Kim H.
    Journal of Institute of Control, Robotics and Systems, 2019, 25 (09): : 795 - 801
  • [48] 3D LiDAR SLAM: A survey
    Zhang, Yongjun
    Shi, Pengcheng
    Li, Jiayuan
    PHOTOGRAMMETRIC RECORD, 2024, 39 (186): : 457 - 517
  • [49] Development of Autonomous Driving System using Programmable SoCs
    Tanaka, Tomonari
    Ikeno, Itsuki
    Tsuruoka, Riku
    Kuchiba, Takumi
    Liao, Wang
    Mitsuyama, Yukio
    2019 INTERNATIONAL CONFERENCE ON FIELD-PROGRAMMABLE TECHNOLOGY (ICFPT 2019), 2019, : 453 - 456
  • [50] Performance evaluation of 2D LiDAR SLAM algorithms in simulated orchard environments
    Li, Qiujie
    Zhu, Hongyi
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 221
12345