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
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