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

Fuente: arXiv
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Autori principali: Ma, Yongxin, Xu, Jie, Yuan, Shenghai, Zhi, Tian, Yu, Wenlu, Zhou, Jun, Xie, Lihua
Natura: Preprint
Pubblicazione: 2025
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author Ma, Yongxin
Xu, Jie
Yuan, Shenghai
Zhi, Tian
Yu, Wenlu
Zhou, Jun
Xie, Lihua
author_facet Ma, Yongxin
Xu, Jie
Yuan, Shenghai
Zhi, Tian
Yu, Wenlu
Zhou, Jun
Xie, Lihua
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_19506
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments
Ma, Yongxin
Xu, Jie
Yuan, Shenghai
Zhi, Tian
Yu, Wenlu
Zhou, Jun
Xie, Lihua
Robotics
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.
title MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments
topic Robotics
url https://arxiv.org/abs/2503.19506