Balancing Accuracy and Efficiency for Large-Scale SLAM: A Minimal Subset Approach for Scalable Loop Closures

Nikolaos Stathoulopoulos, Christoforos Kanellakis, George Nikolakopoulos

Keywords: Robotics and Embodied AI, Localization, SLAM, Loop Closures

TL;DR: This paper proposes MSA, an online keyframe sampler for LiDAR SLAM that minimizes redundancy and preserves information with the feature space and improves localization accuracy, and loop closure efficiency, without manual tuning.

Abstract:

Typical LiDAR SLAM architectures feature a front-end for odometry estimation and a back-end for refining and optimizing the trajectory and map, commonly through loop closures. However, loop closure detection in large-scale missions presents significant computational challenges due to the need to identify, verify, and process numerous candidate pairs for pose graph optimization.
Keyframe sampling bridges the front-end and back-end by selecting frames for storing and processing during global optimization. This article proposes an online keyframe sampling approach that constructs the pose graph using the most impactful keyframes for loop closure.
We introduce the Minimal Subset Approach (MSA), which optimizes two key objectives: redundancy minimization and information preservation, implemented within a sliding window framework. By operating in the feature space rather than 3-D space, MSA efficiently reduces redundant keyframes while retaining essential information.
In sum, evaluations on diverse public datasets show that the proposed approach outperforms naive methods in reducing false positive rates in place recognition, while delivering superior ATE and RPE in metric localization, without the need for manual parameter tuning. Additionally, MSA demonstrates efficiency and scalability by reducing memory usage and computational overhead during loop closure detection and pose graph optimization.

This work is submitted in the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) and a preprint version is available at https://arxiv.org/abs/2501.01791

Submission Number: 146