无IMU/GNSS条件下树木环境中基于2D激光雷达的鲁棒SLAM
Robust 2D lidar-based SLAM in arboreal environments without IMU/GNSS
- Department of Electrical Engineering（电气工程系）
- Pontificia Universidad Católica de Chile（智利天主教大学）
- Department of Mechanical Engineering（机械工程系）
- School of Mechanical and Mechatronic Engineering（机械与机电工程学院）
- University of Technology Sydney（悉尼技术大学）
- Harper Adams University（哈珀·亚当斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对树木环境中GNSS信号遮挡导致的SLAM难题，提出基于2D激光雷达和改进Hausdorff距离的鲁棒扫描匹配方法，在无GNSS场景下精度优于A-LOAM等现有算法，助力精准农业自主导航。
AI中文摘要：
在森林或乔木果园环境中，移动机器人的同时定位与建图（SLAM）方法仍面临挑战，因为树冠会遮挡全球导航卫星系统（GNSS）信号。与室内环境不同，这类农业环境还存在叶片运动、光照变化等户外变量带来的额外挑战。本文提出一种基于2D激光雷达测量的解决方案，与采用3D激光雷达的方法相比，其所需的处理算力和存储空间更少，成本效益更高。该方法利用改进的Hausdorff距离（MHD）度量，无需复杂的特征提取，就能鲁棒且高精度地完成扫描匹配。研究采用公开数据集并结合多种度量指标验证了该方法的鲁棒性，可为未来研究提供有价值的对比参考。与现有最优算法（尤其是A-LOAM）的对比评估表明，所提方法在GNSS拒止环境中实现了更低的位置误差和角度误差，同时保持了更高的精度和抗扰性。这项工作通过在复杂户外环境中实现可靠的自主导航，推动了精准农业的发展。
英文摘要：
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.
