MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments

2025-03-25

IEEE Transactions on Intelligent Vehicles

AI Summary

In highly degraded environments—such as warehouse logistics and food delivery—where human occlusion, airborne debris, and cooking fumes cause severe LiDAR point cloud sparsity (“over-degradation”), existing LiDAR-inertial SLAM systems suffer catastrophic map drift. To address this, we propose the first multi-map LiDAR-inertial SLAM framework specifically designed for over-degraded scenarios. Our method features: (1) a novel dynamic multi-map mechanism that detects degradation in real time and freezes active maps to prevent error accumulation; (2) a constraint-enhanced cross-map fusion strategy leveraging Scan Context for identifying dormant maps and optimizing pose alignment via overlapping trajectories; and (3) an integrated pipeline comprising an iterative error-state Kalman filter frontend, dynamic initialization, and a graph-optimization backend. Evaluated on public over-degradation datasets and real-world complex environments, our system significantly suppresses drift, achieving high-precision localization and dense mapping. The source code is publicly available.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

SLAM plays a crucial role in automation tasks, such as warehouse logistics, healthcare robotics, and restaurant delivery. These scenes come with various challenges, including navigating around crowds of people, dealing with flying plastic bags that can temporarily blind sensors, and addressing reduced LiDAR density caused by cooking smoke. Such scenarios can result in over-degeneracy, causing the map to drift. To address this issue, this paper presents a multi-map LiDAR-inertial system (MM-LINS) for the first time. The front-end employs an iterated error state Kalman filter for state estimation and introduces a reliable evaluation strategy for degeneracy detection. If over-degeneracy is detected, the active map will be stored into sleeping maps. Subsequently, the system continuously attempts to construct new maps using a dynamic initialization method to ensure successful initialization upon leaving the over-degeneracy. Regarding the back-end, the Scan Context descriptor is utilized to detect inter-map similarity. Upon successful recognition of a sleeping map that shares a common region with the active map, the overlapping trajectory region is utilized to constrain the positional transformation near the edge of the prior map. In response to this, a constraint-enhanced map fusion strategy is proposed to achieve high-precision positional and mapping results. Experiments have been conducted separately on both public datasets that exhibited over-degenerate conditions and in real-world environments. These tests demonstrated the effectiveness of MM-LINS in over-degeneracy environment. Our codes are open-sourced on Github.

Problem

• Addresses SLAM drift in over-degenerate environments
• Detects and manages degeneracy using multi-map system
• Ensures high-precision mapping with constraint-enhanced fusion

Innovation

• Multi-map LiDAR-inertial system for over-degeneracy
• Dynamic initialization for new map construction
• Constraint-enhanced map fusion for high precision