论文标题
语义引导的移动对象分割使用3D激光雷达
Semantics-Guided Moving Object Segmentation with 3D LiDAR
论文作者
论文摘要
移动物体分割（MOS）是区分移动物体（例如移动车辆和行人）与周围静态环境的任务。 MOS的细分精度可能会影响探空仪，地图构造和计划任务。在本文中，我们提出了一个语义引导的卷积神经网络，用于移动对象分割。该网络将依次的LIDAR范围图像作为输入。网络没有直接对移动对象进行分割，而是进行基于单扫描的语义分割和基于多个基于多个基于多的基于多的基于多的移动对象。语义分割模块为MOS模块提供了语义先验，我们提出了相邻的扫描关联（ASA）模块，以将相邻扫描的语义特征转换为相同的坐标系，以完全利用跨扫描语义特征。最后，通过分析转化的特征之间的差异，可以快速获得可靠的MOS结果。 Semantickitti MOS数据集的实验结果证明了我们工作的有效性。
Moving object segmentation (MOS) is a task to distinguish moving objects, e.g., moving vehicles and pedestrians, from the surrounding static environment. The segmentation accuracy of MOS can have an influence on odometry, map construction, and planning tasks. In this paper, we propose a semantics-guided convolutional neural network for moving object segmentation. The network takes sequential LiDAR range images as inputs. Instead of segmenting the moving objects directly, the network conducts single-scan-based semantic segmentation and multiple-scan-based moving object segmentation in turn. The semantic segmentation module provides semantic priors for the MOS module, where we propose an adjacent scan association (ASA) module to convert the semantic features of adjacent scans into the same coordinate system to fully exploit the cross-scan semantic features. Finally, by analyzing the difference between the transformed features, reliable MOS result can be obtained quickly. Experimental results on the SemanticKITTI MOS dataset proves the effectiveness of our work.
