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.

翻译：我们提出了一种新颖的框架，用于在非结构化环境中实现车辆的全局定位和引导式重定位。与现有方法相比，我们的流程不依赖城市固定设施（如车道标线、建筑物）的线索，也不假设车辆必须在道路网络上行驶。相反，我们通过鲁棒地关联和配准车辆局部语义对象地图与紧凑的语义参考地图（该地图可能从其他视角、时间周期和/或模态构建），在城区和非城区环境中均实现了定位。通过基于图的数据关联算法，我们实现了对噪声、异常值和缺失对象的鲁棒性。此外，该流程的引导式重定位能力可在初始全局定位后减轻基于里程计的定位固有的漂移问题。我们在两个公开的真实世界数据集上评估了该流程，以证明其在非城区和城区环境下全局定位的有效性。使用Katwijk Beach行星漫游者数据集展示了该流程在非结构化环境中执行精确全局定位的能力。在KITTI数据集上的演示表明，当在从航拍图像创建的参考地图中进行定位时，序列00上所有35个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，因为它能够使用从不同视角构建的地图在非结构化环境中执行全局定位。