LL-Localizer让机器人在多时段中持续精准定位，适应环境变化。
LL-Localizer: A Life-Long Localization System based on Dynamic i-Octree
- 基于动态i-Octree的增量体素地图，支持实时更新与高效管理。
- 在环境变化或跨映射/未映射区域时仍保持稳定定位，误差接近顶尖LIO系统。
- 适合长期运行的移动机器人，尤其适用于动态变化场景下的导航。
本文提出一种基于增量体素的终生定位方法LL-Localizer，使机器人在多时段模式下利用先验地图实现鲁棒且精确的定位。针对先验地图难以察觉环境变化，且实际运行中机器人可能在已映射与未映射区域间穿梭的问题，系统通过增量体素地图按需更新。为保障实时性能并便于地图管理，采用基于动态八叉树的Dynamic i-Octree组织3D点云，实现局部地图加载与动态更新。实验表明，本系统定位性能稳定且准确，可媲美当前最优的激光惯性里程计（LIO）系统；即使先验地图环境发生改变或机器人跨越映射/未映射区域，系统仍能维持鲁棒准确的定位，无明显退化。演示视频见Bilibili（https://www.bilibili.com/video/BV1faZHYCEkZ）和YouTube（https://youtu.be/UWn7RCb9kA8），代码开源地址：https://github.com/M-Evanovic/LL-Localizer。
原文摘要 · Abstract (English)
This paper proposes an incremental voxel-based life-long localization method, LL-Localizer, which enables robots to localize robustly and accurately in multi-session mode using prior maps. Meanwhile, considering that it is difficult to be aware of changes in the environment in the prior map and robots may traverse between mapped and unmapped areas during actual operation, we will update the map when needed according to the established strategies through incremental voxel map. Besides, to ensure high performance in real-time and facilitate our map management, we utilize Dynamic i-Octree, an efficient organization of 3D points based on Dynamic Octree to load local map and update the map during the robot's operation. The experiments show that our system can perform stable and accurate localization comparable to state-of-the-art LIO systems. And even if the environment in the prior map changes or the robots traverse between mapped and unmapped areas, our system can still maintain robust and accurate localization without any distinction. Our demo can be found on Blibili (https://www.bilibili.com/video/BV1faZHYCEkZ) and youtube (https://youtu.be/UWn7RCb9kA8) and the program will be available at https://github.com/M-Evanovic/LL-Localizer.
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