An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles

Abraham MONRROY CANO  Eijiro TAKEUCHI  Shinpei KATO  Masato EDAHIRO  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E103-A   No.1   pp.252-264
Publication Date: 2020/01/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.2019TSP0005
Type of Manuscript: Special Section PAPER (Special Section on Intelligent Transport Systems)
Category: 
Keyword: 
LiDAR,  cameras,  sensor fusion,  calibration,  autonomous driving,  ground detection,  

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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.