LiLoc: Lifelong Localization using Adaptive Submap Joining and Egocentric Factor Graph (2409.10172v1)

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.

• The paper introduces LiLoc, a novel framework for robust lifelong robotic localization in dynamic environments using adaptive submap joining and an egocentric factor graph.
• LiLoc employs adaptive submap joining and a coarse-to-fine initialization strategy for efficient spatial information management and accurate initial pose estimation.
• The core Egocentric Factor Graph integrates multiple sensor data streams and a propagation model to achieve superior localization performance and computational balance compared to state-of-the-art methods.

An Expert Analysis of the LiLoc Framework for Lifelong Localization

The paper entitled "LiLoc: Lifelong Localization using Adaptive Submap Joining and Egocentric Factor Graph" introduces a novel approach to enhancing the accuracy and timeliness of robotic localization in dynamic and multi-session environments. With rapidly changing environments such as industrial and underground exploration, robust long-term localization becomes essential for autonomous robotic operations. LiLoc addresses these challenges by leveraging a centralized session with adaptable submap management and incorporates multifaceted constraints through an Egocentric Factor Graph (EFG).

The LiLoc framework offers several key contributions to the field of localization and mapping:

1. Adaptive Submap Joining: LiLoc introduces an adaptive strategy to manage spatial information dynamically, reducing system load by focusing only on the essential elements of past sessions. This is critical for maintaining efficiency in large-scale maps and ensuring timely access to relevant data for accurate localization.
2. Coarse-to-Fine Pose Initialization: This approach improves the accuracy of initial localization estimates by using a combination of vertical recognition and Iterative Closest Point (ICP) refinement, essential for robust subsidiary session initiation.
3. Egocentric Factor Graph with Propagation Model: The proposed EFG module is central to LiLoc's performance, where it integrates IMU preintegration, LiDAR odometry, and scan matching in a joint optimization setup. Of particular interest is the propagation model that binds the prior data to relevant current estimations more effectively, mitigating the reliance on potentially erroneous prior constraints.
4. Mode-Switching Mechanism: The framework intelligently alternates between Relocalization (RLM) and Incremental Localization (ILM) based on the spatial overlap in current scans, optimizing the localization approach as environmental circumstances change.

The authors demonstrate LiLoc's efficacy against existing state-of-the-art methods using both public datasets and custom scenarios. When compared to recent advancements like LIO-SAM and FAST-LIO2, LiLoc exhibits superior localization performance both in terms of absolute trajectory errors and flexibility across various deployment environments. LiLoc's ability to adaptively handle submaps ensures more accurate long-term positioning, positioning it favorably against systems like HDL-Loc and Block-Loc, which fall short under multi-session conditions.

Furthermore, experimental results highlight LiLoc's capacity to achieve a balanced computational time, attributable to sophisticated joint factor-graph optimization and efficient propagation of constraints. This application of multifactor integration within graph-based localization reveals promising directions for future research, particularly in tackling the challenges of multi-robot systems operating in unstructured or unknown environments.

Future work could focus on refining computational efficiencies further, investigating the deployment of LiLoc in active cooperative mapping setups, and exploring the use of alternative sensory data to bolster robustness in localization and mapping tasks. In consequence, LiLoc represents a significant advancement in the quest for enduring, precise localization solutions adaptable to the evolving demands of robotic systems.