SSF-PAN：面向交通场景自主导航的语义场景流感知
SSF-PAN: Semantic Scene Flow-Based Perception for Autonomous Navigation in Traffic Scenarios
AI总结：
提出SSF-PAN方法，通过语义场景流分割与迭代优化框架，结合LiDAR点云实现高效高精度的物体检测、定位与SLAM，支持无图导航，在多个数据集与模拟器中验证其优于传统方法。
摘要：
由于移动物体的干扰，复杂交通场景中的车辆检测与定位面临重大挑战。传统方法通常依赖外点剔除或语义分割，其计算效率和精度较低。提出的SSF-PAN能以高计算效率和精度实现基于LiDAR点云的物体检测/定位及SLAM（同步定位与建图）功能，从而支持无图导航框架。本工作的创新点有三：1）开发一种神经网络，能在具有不同运动特征的场景流中实现静态与动态物体间的分割，即语义场景流（SSF）；2）开发一种迭代框架，可进一步优化输入场景流质量和输出分割结果；3）开发一个基于场景流的导航平台，可在仿真环境中测试SSF感知系统的性能。所提SSF-PAN方法使用SUScape-CARLA和KITTI数据集，以及CARLA模拟器进行验证。实验结果表明，该方法在场景流计算精度、移动物体检测精度、计算效率及自主导航有效性方面均优于传统方法。
英文摘要：
Vehicle detection and localization in complex traffic scenarios pose significant challenges due to the interference of moving objects. Traditional methods often rely on outlier exclusions or semantic segmentations, which suffer from low computational efficiency and accuracy. The proposed SSF-PAN can achieve the functionalities of LiDAR point cloud based object detection/localization and SLAM (Simultaneous Localization and Mapping) with high computational efficiency and accuracy, enabling map-free navigation frameworks. The novelty of this work is threefold: 1) developing a neural network which can achieve segmentation among static and dynamic objects within the scene flows with different motion features, that is, semantic scene flow (SSF); 2) developing an iterative framework which can further optimize the quality of input scene flows and output segmentation results; 3) developing a scene flow-based navigation platform which can test the performance of the SSF perception system in the simulation environment. The proposed SSF-PAN method is validated using the SUScape-CARLA and the KITTI datasets, as well as on the CARLA simulator. Experimental results demonstrate that the proposed approach outperforms traditional methods in terms of scene flow computation accuracy, moving object detection accuracy, computational efficiency, and autonomous navigation effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
