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An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles

Abraham MONRROY CANO, Eijiro TAKEUCHI, Shinpei KATO, Masato EDAHIRO

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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

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