An open multi-sensor fusion toolbox for autonomous vehicles

Abraham Monrroy Cano*, Eijiro Takeuchi, Shinpei Kato, Masato Edahiro

*この研究の対応する著者

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)252-264
ページ数13
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E103A
1
DOI
出版ステータスPublished - 2020
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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