Research on Calibration and Hardware Acceleration of Multi-Sensor Fusion Perception System in Autonomous Driving

Engineering notes

Key topics: autonomous driving, autonomous vehicle, lidar, sensor fusion, multi-sensor fusion, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This paper focuses on advancing multi-sensor fusion calibration techniques for autonomous vehicles, with the goal of enhancing vehicular safety and reliability across diverse operational environments.Centering on LiDAR, cameras, and auxiliary sensors, this paper first analyzes their performance attributes and then designs dedicated intrinsic and extrinsic parameter calibration methodologies. Additionally, a natural-scene-based site selection strategy for calibration is proposed. Intrinsic calibration determines each sensors' internal parameters, and extrinsic calibration establishes the transformation relationship between each sensor and the global coordinate system. Experimental results show that multi-sensor fusion can improves the accuracy of environmental perception, especially in target detection, positioning, and scene reconstruction. This paper shows that high-precision calibration is the foundation for efficient multi-sensor fusion, and is important to improve the safety and enhance the performance of autonomous vehicles. Future research efforts will focus on improving calibration algorithms and exploring new fusion approaches to handle complex dynamic scenarios.

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