CMSG：用于自动驾驶车辆重定位的跨媒体语义图特征匹配算法
CMSG Cross-Media Semantic-Graph Feature Matching Algorithm for Autonomous Vehicle Relocalization
AI总结：
提出跨媒体语义图特征匹配算法CMSG，结合图像与激光雷达地图，利用语义特征和图网络提升自动驾驶车辆重定位精度，在KITTI数据集上达到25 FPS且精度优于单传感器方法。
摘要：
重定位是基于地图的定位算法的基础。基于相机和激光雷达地图的方法因其在不同场景下的鲁棒性而普遍存在。通常，使用相同传感器进行建图和定位具有更好的精度，因为相同类型数据之间的特征匹配更容易。然而，由于相机缺乏3D信息以及激光雷达的高成本，跨媒体方法正在发展，这些方法结合了实时图像数据和激光雷达地图。尽管不同媒体之间的特征匹配具有挑战性，但我们相信跨媒体是自动驾驶车辆重定位的趋势，因为其低成本且精度可与基于相同传感器的方法相媲美。在本文中，我们提出了CMSG，一种用于自动驾驶车辆重定位任务的新型跨媒体算法。利用语义特征来更好地解释点云与图像特征之间的相关性。此外，引入了抽象的语义图节点，并集成了图网络架构以更好地提取语义特征的相似性。在KITTI里程计数据集上进行了验证实验。我们的结果表明，在NVIDIA 1080 Ti GPU上以25 FPS的速度，CMSG可以达到与当前基于单传感器的方法相当甚至更好的精度。
英文摘要：
Relocalization is the basis of map-based localization algorithms. Camera and LiDAR map-based methods are pervasive since their robustness under different scenarios. Generally, mapping and localization using the same sensor have better accuracy since matching features between the same type of data is easier. However, due to the camera's lack of 3D information and the high cost of LiDAR, cross-media methods are developing, which combined live image data and Lidar map. Although matching features between different media is challenging, we believe cross-media is the tendency for AV relocalization since its low cost and accuracy can be comparable to the same-sensor-based methods. In this paper, we propose CMSG, a novel cross-media algorithm for AV relocalization tasks. Semantic features are utilized for better interpretation the correlation between point clouds and image features. What's more, abstracted semantic graph nodes are introduced, and a graph network architecture is integrated to better extract the similarity of semantic features. Validation experiments are conducted on the KITTI odometry dataset. Our results show that CMSG can have comparable or even better accuracy compared to current single-sensor-based methods at a speed of 25 FPS on NVIDIA 1080 Ti GPU.
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。
