IEICE TRANSACTIONS on Fundamentals
An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.1 pp.252-264
- Publication Date
- 2020/01/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019TSP0005
- Type of Manuscript
- Special Section PAPER (Special Section on Intelligent Transport Systems)
- Category
Authors
Abraham MONRROY CANO
Nagoya University
Eijiro TAKEUCHI
Nagoya University
Shinpei KATO
The University of Tokyo
Masato EDAHIRO
Nagoya University
Keyword
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Abraham MONRROY CANO, Eijiro TAKEUCHI, Shinpei KATO, Masato EDAHIRO, "An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 1, pp. 252-264, January 2020, doi: 10.1587/transfun.2019TSP0005.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019TSP0005/_p
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@ARTICLE{e103-a_1_252,
author={Abraham MONRROY CANO, Eijiro TAKEUCHI, Shinpei KATO, Masato EDAHIRO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles},
year={2020},
volume={E103-A},
number={1},
pages={252-264},
abstract={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.},
keywords={},
doi={10.1587/transfun.2019TSP0005},
ISSN={1745-1337},
month={January},}
Copy
TY - JOUR
TI - An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 252
EP - 264
AU - Abraham MONRROY CANO
AU - Eijiro TAKEUCHI
AU - Shinpei KATO
AU - Masato EDAHIRO
PY - 2020
DO - 10.1587/transfun.2019TSP0005
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2020
AB - 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.
ER -
English
