一种面向智能驾驶的毫米波雷达与激光雷达融合的鲁棒感知算法

A Robust Perception Algorithm Based on a Radar and LiDAR for Intelligent Driving

基于多传感器融合感知是实现汽车智能驾驶的关键技术之一,已成为智能驾驶领域的热点问题。然而,由于毫米波雷达分辨率有限,且易受噪声、杂波、多径等因素的干扰,激光雷达易受天气的影响,现有的融合算法很难实现这两种传感器数据的精确融合,得到鲁棒的结果。针对智能驾驶中准确鲁棒的感知问题,该文提出了一种融合毫米波雷达和激光雷达鲁棒的感知算法。使用基于特征的两步配准的空间校正新方法,实现了三维激光点云和二维毫米波雷达点云精确的空间同步。使用改进的毫米波雷达滤波算法,减少了噪声、多径等对毫米波雷达点云的影响。然后根据该文提出的新颖的融合方法对两种传感器的数据进行融合,得到准确鲁棒的感知结果,解决了烟雾对激光性能影响的问题。最后,通过实际场景的实验测试,验证了该文算法的有效性和鲁棒性,即使在烟雾等极端环境中仍然能够实现准确和鲁棒的感知。使用该文融合方法建立的环境地图更加精确,得到的定位结果比使用单一传感器的定位误差减少了至少50%。

Multi-sensor fusion perception is one of the key technologies to realize intelligent automobile driving,and it has become a hot issue in the field of intelligent driving.However,because of the limited resolution of millimeter-wave radars,the interference of noise,clutter,and multipath,and the influence of weather on LiDAR,the existing fusion algorithm cannot easily achieve accurate fusion of the data of two sensors and obtain robust results.To address the problem of accurate and robust perception in intelligent driving,this study proposes a robust perception algorithm that combines millimeter-wave radar and LiDAR.Using a new method of spatial correction based on feature-based two-step registration,the precise spatial synchronization of the 3D LiDAR and 2D radar point clouds is realized.The improved millimeter-wave radar filtering algorithm is used to reduce the influence of noise and multipath on the radar point cloud.Then,according to the novel fusion method proposed in this study,the data of the two sensors are fused to obtain accurate and robust sensing results,which solves the problem of the influence of smoke on LiDAR performance.Finally,we conducted multiple sets of experiments in a real environment to verify the effectiveness and robustness of our method.Even in extreme environments such as smoke,we can still achieve accurate positioning and robust mapping.The environment map established by the fusion method proposed in this study is more accurate than that established by a single sensor.Moreover,the location error obtained can be reduced by at least 50%.