LiLoc: Lifelong Localization using Adaptive Submap Joining and Egocentric Factor Graph

TL;DR

LiLoc is a graph-based lifelong localization framework that combines adaptive submap joining and an egocentric factor graph to improve accuracy and efficiency in autonomous localization tasks.

Contribution

The paper introduces a novel framework integrating adaptive submap joining and an egocentric factor graph for improved lifelong localization accuracy and efficiency.

Findings

Achieves accurate localization on public and custom datasets.

Outperforms state-of-the-art methods in localization accuracy.

Supports flexible relocalization and incremental localization modes.

Abstract

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.