Multi-Mapcher: Loop Closure Detection-Free Heterogeneous LiDAR Multi-Session SLAM Leveraging Outlier-Robust Registration for Autonomous Vehicles

TL;DR

Multi-Mapcher introduces a novel multi-session SLAM framework for heterogeneous LiDAR sensors that eliminates the need for loop closure detection by using large-scale map registration and robust pose graph optimization, improving accuracy and speed.

Contribution

It proposes a new MSS method that relies on map-to-map registration instead of loop closure detection, enabling effective multi-session SLAM across diverse LiDAR sensors.

Findings

Outperforms existing MSS methods in accuracy across various sensors.

Faster processing time compared to state-of-the-art approaches.

Demonstrates robustness to sensor heterogeneity and field of view differences.

Abstract

As various 3D light detection and ranging (LiDAR) sensors have been introduced to the market, research on multi-session simultaneous localization and mapping (MSS) using heterogeneous LiDAR sensors has been actively conducted. Existing MSS methods mostly rely on loop closure detection for inter-session alignment; however, the performance of loop closure detection can be potentially degraded owing to the differences in the density and field of view (FoV) of the sensors used in different sessions. In this study, we challenge the existing paradigm that relies heavily on loop detection modules and propose a novel MSS framework, called Multi-Mapcher, that employs large-scale map-to-map registration to perform inter-session initial alignment, which is commonly assumed to be infeasible, by leveraging outlier-robust 3D point cloud registration. Next, after finding inter-session loops by radius search based on the assumption that the inter-session initial alignment is sufficiently precise, anchor node-based robust pose graph optimization is employed to build a consistent global map. As demonstrated in our experiments, our approach shows substantially better MSS performance for various LiDAR sensors used to capture the sessions and is faster than state-of-the-art approaches. Our code is available at https://github.com/url-kaist/multi-mapcher.