利用不同视角构建的语义对象地图在非结构化环境中进行全局定位
Global Localization in Unstructured Environments using Semantic Object Maps Built from Various Viewpoints
AI总结：
本文提出一种基于语义对象地图关联与配准的全局定位框架，通过图数据关联和引导式重定位，在非结构化环境中利用不同视角构建的参考地图实现鲁棒定位，并在两个真实数据集上验证了有效性。
摘要：
我们提出了一种用于车辆在非结构化环境中进行全局定位和引导式重定位的新框架。与现有方法相比，我们的流程不依赖城市设施（例如车道标线、建筑物）提供的线索，也不做要求车辆必须在道路网络上行驶的假设。相反，我们通过将车辆的局部语义对象地图与一个紧凑的语义参考地图进行鲁棒关联和配准，在城市和非城市环境中均实现定位，该参考地图可能由其他视角、时间段和/或模态构建而成。通过我们基于图的数据关联算法，实现了对噪声、离群点和缺失对象的鲁棒性。此外，我们的流程的引导式重定位能力减轻了初始全局定位后基于里程计的定位中固有的漂移。我们在两个公开可用的真实世界数据集上评估了我们的流程，以证明其在非城市和城市环境中进行全局定位的有效性。Katwijk Beach Planetary Rover 数据集用于展示我们的流程在非结构化环境中执行精确全局定位的能力。在 KITTI 数据集上的演示中，当在由航空图像创建的参考地图中进行定位时，Sequence 00 上所有 35 次定位事件的平均位姿误差为 3.8 米。与现有工作相比，我们的流程更具通用性，因为它可以使用不同视角构建的地图在非结构化环境中执行全局定位。
英文摘要：
We present a novel framework for global localization and guided relocalization of a vehicle in an unstructured environment. Compared to existing methods, our pipeline does not rely on cues from urban fixtures (e.g., lane markings, buildings), nor does it make assumptions that require the vehicle to be navigating on a road network. Instead, we achieve localization in both urban and non-urban environments by robustly associating and registering the vehicle's local semantic object map with a compact semantic reference map, potentially built from other viewpoints, time periods, and/or modalities. Robustness to noise, outliers, and missing objects is achieved through our graph-based data association algorithm. Further, the guided relocalization capability of our pipeline mitigates drift inherent in odometry-based localization after the initial global localization. We evaluate our pipeline on two publicly-available, real-world datasets to demonstrate its effectiveness at global localization in both non-urban and urban environments. The Katwijk Beach Planetary Rover dataset is used to show our pipeline's ability to perform accurate global localization in unstructured environments. Demonstrations on the KITTI dataset achieve an average pose error of 3.8m across all 35 localization events on Sequence 00 when localizing in a reference map created from aerial images. Compared to existing works, our pipeline is more general because it can perform global localization in unstructured environments using maps built from different viewpoints.
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。
