Efficiently Closing Loops in LiDAR-Based SLAM Using Point Cloud Density Maps

2025-01-13

AI Summary

Addressing the challenge of robust loop closure detection in outdoor LiDAR-SLAM across heterogeneous platforms and multi-sensor setups—particularly under perceptual aliasing induced by varying LiDAR models, scanning patterns, field-of-view configurations, motion trajectories, and structurally similar environments—this paper proposes a general-purpose loop closure detection framework. Our method introduces three key innovations: (1) a density-preserving bird’s-eye-view (BEV) projection that jointly maintains geometric consistency and point cloud distribution characteristics; (2) a ground-plane alignment module adapted to non-planar motion, enhancing cross-platform pose estimation robustness; and (3) an ORB feature matching mechanism leveraging self-similarity-based pruning to suppress interference from repetitive structures. Evaluated on both public and custom-built datasets, our approach achieves high-precision loop closure detection, long-term localization stability, and accurate cross-platform multi-map registration—without reliance on specific LiDAR hardware or motion priors.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Consistent maps are key for most autonomous mobile robots. They often use SLAM approaches to build such maps. Loop closures via place recognition help maintain accurate pose estimates by mitigating global drift. This paper presents a robust loop closure detection pipeline for outdoor SLAM with LiDAR-equipped robots. The method handles various LiDAR sensors with different scanning patterns, field of views and resolutions. It generates local maps from LiDAR scans and aligns them using a ground alignment module to handle both planar and non-planar motion of the LiDAR, ensuring applicability across platforms. The method uses density-preserving bird's eye view projections of these local maps and extracts ORB feature descriptors from them for place recognition. It stores the feature descriptors in a binary search tree for efficient retrieval, and self-similarity pruning addresses perceptual aliasing in repetitive environments. Extensive experiments on public and self-recorded datasets demonstrate accurate loop closure detection, long-term localization, and cross-platform multi-map alignment, agnostic to the LiDAR scanning patterns, fields of view, and motion profiles.

Problem

• Lidar-based Localization
• Outdoor Environment
• Similar Environment Recognition

Innovation

• PointCloud Density Map
• Lidar SLAM
• Binary Search Tree