激光雷达
校准
传感器融合
计算机科学
遥感
融合
计算机视觉
人工智能
组分(热力学)
智能传感器
无线传感器网络
地理
数学
物理
热力学
哲学
统计
语言学
计算机网络
作者
Xinyu Zhang,Yijin Xiong,Qianxin Qu,Shifan Zhu,Shichun Guo,Dafeng Jin,Guoying Zhang,Haibing Ren,Jun Li
标识
DOI:10.1109/tim.2023.3341122
摘要
In intelligent driving systems, the multisensor fusion perception system comprising multiple cameras and LiDAR has become a crucial component. It is essential to have stable extrinsic parameters among devices in a multisensor fusion system to achieve all-weather sensing with no blind zones. However, prolonged vehicle usage can result in immeasurable sensor offsets that lead to perception deviations. To this end, we have studied multisensor unified calibration, rather than the calibration between a single pair of sensors as previously done. Benefiting from the mutually constrained pose between different sensor pairs, the method improves calibration accuracy by around 20% compared to calibration for a pair of sensors. The study can serve as a foundation for multisensor unified calibration, enabling the overall automatic optimization of all camera and LiDAR sensors onboard a vehicle within a single framework.
科研通智能强力驱动
Strongly Powered by AbleSci AI