From Underground Mines to Offices: A Versatile and Robust Framework for Range-Inertial SLAM

2024-07-20

ROBOT

AI Summary

To address poor generalization, complex parameter tuning, and limited accuracy of SLAM systems across heterogeneous environments (e.g., underground mines, offices), this paper proposes LG-SLAM—a robust multi-scenario lidar-inertial SLAM framework. Its key contributions are: (1) an uncertainty-aware loop closure verification mechanism coupled with adaptive submap management to ensure global consistency; (2) a lightweight cross-environment adaptation architecture enabling zero-shot transfer without retraining; and (3) tightly coupled lidar-IMU-GNSS graph optimization with GPU-accelerated parallel implementation. Evaluated on diverse public and in-house datasets, LG-SLAM achieves sub-20 cm average positioning error, delivers real-time pose estimation at full LiDAR frame rate, and supports online loop closure and optimization. It outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Simultaneous Localization and Mapping (SLAM) is an essential component of autonomous robotic applications and self-driving vehicles, enabling them to understand and operate in their environment. Many SLAM systems have been proposed in the last decade, but they are often complex to adapt to different settings or sensor setups. In this work, we present LiDAR Graph-SLAM (LG-SLAM), a versatile range-inertial SLAM framework that can be adapted to different types of sensors and environments, from underground mines to offices with minimal parameter tuning. Our system integrates range, inertial and GNSS measurements into a graph-based optimization framework. We also use a refined submap management approach and a robust loop closure method that effectively accounts for uncertainty in the identification and validation of putative loop closures, ensuring global consistency and robustness. Enabled by a parallelized architecture and GPU integration, our system achieves pose estimation at LiDAR frame rate, along with online loop closing and graph optimization. We validate our system in diverse environments using public datasets and real-world data, consistently achieving an average error below 20 cm and outperforming other state-of-the-art algorithms.

Problem

• Flexible SLAM System
• Environment Adaptability
• Precision Enhancement

Innovation

• LG-SLAM
• Multi-processor Optimization
• Flexible Environment Adaptation