This paper proposes a versatile graph-based lifelong localization framework, LiLoc, which enhances its timeliness by maintaining a single central session while improves the accuracy through multi-modal factors between the central and subsidiary sessions. First, an adaptive submap joining strategy is employed to generate prior submaps (keyframes and poses) for the central session, and to provide priors for subsidiaries when constraints are needed for robust localization. Next, a coarse-to-fine pose initialization for subsidiary sessions is performed using vertical recognition and ICP refinement in the global coordinate frame. To elevate the accuracy of subsequent localization, we propose an egocentric factor graph (EFG) module that integrates the IMU preintegration, LiDAR odometry and scan match factors in a joint optimization manner. Specifically, the scan match factors are constructed by a novel propagation model that efficiently distributes the prior constrains as edges to the relevant prior pose nodes, weighted by noises based on keyframe registration errors. Additionally, the framework supports flexible switching between two modes: relocalization (RLM) and incremental localization (ILM) based on the proposed overlap-based mechanism to select or update the prior submaps from central session. The proposed LiLoc is tested on public and custom datasets, demonstrating accurate localization performance against state-of-the-art methods. Our codes will be publicly available on https://github.com/Yixin-F/LiLoc.

翻译：本文提出了一种通用的基于图的全生命周期定位框架LiLoc，该框架通过维护单一中心会话提升时效性，同时利用中心会话与辅助会话间的多模态因子提高精度。首先，采用自适应子图拼接策略为中心会话生成先验子图（关键帧与位姿），并在需要约束以实现鲁棒定位时为辅助会话提供先验。其次，在全局坐标系中通过垂直识别与ICP精细化实现辅助会话的由粗到精位姿初始化。为提升后续定位精度，我们提出自我中心因子图（EFG）模块，以联合优化方式集成IMU预积分、激光雷达里程计与扫描匹配因子。具体而言，扫描匹配因子通过一种新颖的传播模型构建，该模型以前验位姿节点间带权边的形式高效分配先验约束，其权重噪声基于关键帧配准误差确定。此外，本框架支持基于重叠度机制从中心会话选择或更新先验子图，实现重定位（RLM）与增量定位（ILM）两种模式的灵活切换。所提出的LiLoc在公开与定制数据集上进行了测试，相较于前沿方法展现出精确的定位性能。代码将在https://github.com/Yixin-F/LiLoc公开。