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

TL;DR

This paper introduces a robust 2D lidar-based SLAM method for autonomous navigation in forest environments, overcoming GNSS limitations without relying on IMU or 3D sensors, and demonstrating superior accuracy and resilience.

Contribution

The paper presents a novel 2D lidar SLAM approach using modified Hausdorff distance, validated on public datasets, that outperforms state-of-the-art algorithms in GNSS-denied arboreal environments.

Findings

Lower positional and angular errors compared to A-LOAM

High robustness in GNSS-denied outdoor environments

Validated on diverse public datasets

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.