LL-Localizer: A Lifelong Localization System Based on Dynamic i-Octree

Abstract

This article proposes an incremental voxel-based lifelong localization method, LL-Localizer, which enables robots to localize robustly and accurately in multisession 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 an incremental voxel map. In addition, to ensure high performance in real-time and facilitate our map management, we utilize Dynamic i-Octree, an efficient organization of 3-D points based on a dynamic Octree, to load the 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 demos can be found on Blibili (<uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.bilibili.com/video/BV1faZHYCEkZ</uri>) and youtube (<uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/UWn7RCb9kA8</uri>), and the program will be available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/M-Evanovic/LL-Localizer</uri>