Robust 2D lidar-based SLAM in arboreal environments without IMU/GNSS

2025-05-16

AI Summary

For outdoor agricultural scenarios—such as orchard and forest environments—characterized by GNSS denial, dynamic foliage occlusion, and significant illumination variation, this paper proposes a robust SLAM method relying solely on a low-cost 2D LiDAR. The method eliminates reliance on IMU, GNSS, or hand-crafted features by introducing a modified Hausdorff distance (MHD) for featureless scan matching—a novel application in LiDAR SLAM. Integrated with lightweight loop closure detection and pose-graph optimization, the framework enhances both robustness and accuracy of pose estimation. Evaluated on a public forest dataset, the proposed approach achieves a 32% reduction in translational error and a 27% reduction in rotational error compared to A-LOAM, demonstrating significantly improved localization accuracy and system resilience under dense, dynamic vegetation.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

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

Abstract

Simultaneous localization and mapping (SLAM) approaches for mobile robots remains challenging in forest or arboreal fruit farming environments, where tree canopies obstruct Global Navigation Satellite Systems (GNSS) signals. Unlike indoor settings, these agricultural environments possess additional challenges due to outdoor variables such as foliage motion and illumination variability. This paper proposes a solution based on 2D lidar measurements, which requires less processing and storage, and is more cost-effective, than approaches that employ 3D lidars. Utilizing the modified Hausdorff distance (MHD) metric, the method can solve the scan matching robustly and with high accuracy without needing sophisticated feature extraction. The method's robustness was validated using public datasets and considering various metrics, facilitating meaningful comparisons for future research. Comparative evaluations against state-of-the-art algorithms, particularly A-LOAM, show that the proposed approach achieves lower positional and angular errors while maintaining higher accuracy and resilience in GNSS-denied settings. This work contributes to the advancement of precision agriculture by enabling reliable and autonomous navigation in challenging outdoor environments.

Problem

• SLAM in GNSS-denied arboreal environments using 2D lidar
• Robust scan matching without IMU/GNSS or complex feature extraction
• Improving accuracy and resilience in foliage-rich outdoor settings

Innovation

• 2D lidar SLAM without IMU/GNSS
• Modified Hausdorff distance for scan matching
• Higher accuracy than A-LOAM in GNSS-denied settings