一种实例增强的激光雷达运动目标分割方法
A Moving-object Segmentation Method Based on Instance Enhancement on LiDAR Data
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摘要: 针对复杂动态环境中基于激光雷达数据的运动目标分割法实时性较差、逐点分割准确性较低等问题, 提出一种基于实例增强的激光雷达运动目标分割方法。首先, 将3维激光雷达点云转换为2维鸟瞰图, 通过计算当前点云帧与历史点云帧之间的鸟瞰图残差实现运动特征的快速提取。然后结合运动特征与3维点云空间特征提取实例特征, 利用实例信息对属于同一实例的点云进行一致性运动分割, 提高激光雷达逐点运动分割准确性。利用开源数据集SemanticKITTI对该方法进行了测试, 实验结果表明, 本文方法的交并比指标为72.2%;消融实验表明, 实例信息增强后交并比指标较基础模型提高了8.7%, 运动目标分割的实时性和准确性较当前先进方法都有更优或相当的表现, 验证了利用实例信息增强法来完成多感知任务的有效性。Abstract: Aiming at the problem of poor real-time performance and low point-wise segmentation accuracy in LiDAR-based moving-object segmentation for robots in a complex dynamic environment, a moving-object segmentation method based on instance enhancement on LiDAR data is proposed. Firstly, motion features are extracted quickly by calculating the residuals between the current frame and historical frames of the 2D BEV (bird's eye view) images, which are converted from the 3D LiDAR point clouds. Then, instance features are extracted by integrating the motion features with the spatial characteristics of the 3D point cloud. Using the instance information, consistent motion segmentation is achieved for point clouds belonging to the same instance, and thus improving the accuracy of LiDAR-based moving-object point-wise segmentation. Experiments are carried out on the open-source dataset SemanticKITTI, and the experimental results show that the proposed method achieves an IoU (intersection over union) score of 72.2%. The ablation experiments indicate that the IoU score is increased by 8.7% over the baseline model after enhancement with instance information. The real-time performance and the moving-object segmentation accuracy are superior or comparable to the current advanced methods, validating the effectiveness of utilizing instance information enhancement for multi-perception tasks.