An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles

被引:0
|
作者
Cano, Abraham Monrroy [1 ]
Takeuchi, Eijiro [1 ]
Kato, Shinpei [2 ]
Edahiro, Masato [1 ]
机构
[1] Nagoya Univ, Grad Sch Informat Sci, Nagoya, Aichi 4648603, Japan
[2] Univ Tokyo, Grad Sch Informat Sci & Technol, Tokyo 1138656, Japan
关键词
LiDAR; cameras; sensor fusion; calibration; autonomous driving; ground detection; REGISTRATION;
D O I
10.1587/transfun.2019TSP0005
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
We present an accurate and easy-to-use multi-sensor fusion toolbox for autonomous vehicles. It includes a 'target-less' multi-LiDAR (Light Detection and Ranging), and Camera-LiDAR calibration, sensor fusion, and a fast and accurate point cloud ground classifier. Our calibration methods do not require complex setup procedures, and once the sensors are calibrated, our framework eases the fusion of multiple point clouds, and cameras. In addition we present an original real-time ground-obstacle classifier, which runs on the CPU, and is designed to be used with any type and number of LiDARs. Evaluation results on the KITTI dataset confirm that our calibration method has comparable accuracy with other state-of-the-art contenders in the benchmark.
引用
收藏
页码:252 / 264
页数:13
相关论文
共 50 条
  • [1] Environment recognition based on multi-sensor fusion for autonomous driving vehicles
    Weon I.-S.
    Lee S.-G.
    Journal of Institute of Control, Robotics and Systems, 2019, 25 (02): : 125 - 131
  • [2] Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
    Jahromi, Babak Shahian
    Tulabandhula, Theja
    Cetin, Sabri
    SENSORS, 2019, 19 (20)
  • [3] OpenCalib: A multi-sensor calibration toolbox for autonomous driving
    Yan, Guohang
    Zhuochun, Liu
    Wang, Chengjie
    Shi, Chunlei
    Wei, Pengjin
    Cai, Xinyu
    Ma, Tao
    Liu, Zhizheng
    Zhong, Zebin
    Liu, Yuqian
    Zhao, Ming
    Ma, Zheng
    Li, Yikang
    SOFTWARE IMPACTS, 2022, 14
  • [4] Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles
    Yeong, De Jong
    Panduru, Krishna
    Walsh, Joseph
    SENSORS, 2025, 25 (03)
  • [5] Multi-Sensor Fusion for Navigation and Mapping in Autonomous Vehicles: Accurate Localization in Urban Environments
    Li Qingqing
    Queralta, Jorge Pena
    Tuan Nguyen Gia
    Zhuo Zou
    Westerlund, Tomi
    UNMANNED SYSTEMS, 2020, 8 (03) : 229 - 237
  • [6] Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review
    Yeong, De Jong
    Velasco-Hernandez, Gustavo
    Barry, John
    Walsh, Joseph
    SENSORS, 2021, 21 (06) : 1 - 37
  • [7] A Review of Environmental Perception Technology Based on Multi-Sensor Information Fusion in Autonomous Driving
    Yang, Boquan
    Li, Jixiong
    Zeng, Ting
    WORLD ELECTRIC VEHICLE JOURNAL, 2025, 16 (01):
  • [8] Feature Map Transformation for Multi-sensor Fusion in Object Detection Networks for Autonomous Driving
    Schroder, Enrico
    Braun, Sascha
    Mahlisch, Mirko
    Vitay, Julien
    Hamker, Fred
    ADVANCES IN COMPUTER VISION, VOL 2, 2020, 944 : 118 - 131
  • [9] SaLsA Streams: Dynamic Context Models for Autonomous Transport Vehicles based on Multi-Sensor Fusion
    Kuka, Christian
    Bolles, Andre
    Funk, Alexander
    Eilers, Soenke
    Schweigert, Soeren
    Gerwinn, Sebastian
    Nicklas, Daniela
    2013 IEEE 14TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2013), VOL 1, 2013, : 263 - 266
  • [10] Calibration of multi-sensor fusion for autonomous vehicle system
    Lu, Yongkang
    Zhong, Wenjian
    Li, Yanzhou
    INTERNATIONAL JOURNAL OF VEHICLE DESIGN, 2023, 91 (1-3) : 248 - 262