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与引导式重定位。与现有方法相比，本流程不依赖城市基础设施特征（如车道标线、建筑物），也不假设车辆必须在道路网络中行驶。相反，通过稳健地关联并配准车辆本地语义对象地图与紧凑的语义参考地图（该参考地图可能从其他视角、时间段或模态构建），我们能在城市与非城市环境中实现定位。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，本流程的引导式重定位能力可在初始全局定位后，有效减轻基于里程计定位的漂移问题。我们在两个公开真实世界数据集上评估了本流程，证明其在非城市与城市环境中实现全局定位的有效性。利用Katwijk海滩行星探测车数据集，展示了本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，当使用航空影像构建的参考地图对序列00进行定位时，全部35个定位事件的平均位姿误差为3.8米。与现有工作相比，本流程更具通用性，因为它能利用不同视角构建的地图在非结构化环境中实现全局定位。