面向实时、大规模且GNSS中断的森林测绘的多传感器地面SLAM
Multi-Sensor Terrestrial SLAM for Real-Time, Large-Scale, and GNSS-Interrupted Forest Mapping
- Faculty of Science and Technology, Norwegian University of Life Sciences (NMBU)（挪威生命科学大学科学与技术学院）
- Norwegian Institute of Bioeconomy Research (NIBIO)（挪威生物经济研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对大规模GNSS中断森林环境中实时资源清查难的问题，提出融合3D激光雷达与IMU的多传感器地面SLAM方案，采用分层无监督聚类实现树木检测与胸径测量，支持实时/离线运行，输入频率优于常规算法，经实地测试精度达到先进水平。
AI中文摘要：
森林作为生态系统的关键组成部分，需要有效的监测与管理。然而，在大规模且GNSS信号中断的森林环境中开展实时森林资源清查长期以来都是一项艰巨的挑战。本文提出了一种新颖的解决方案，利用机器人技术与传感器融合技术克服上述挑战，实现精度更高、效率更优的实时森林资源清查。该方案包含一种全新的SLAM（同时定位与建图）算法，仅通过3D激光雷达与IMU（惯性测量单元）的连续扫描，即可构建大规模林分的精确3D地图，并详细估算树木数量及对应的DBH（胸径）。该方法采用分层无监督聚类算法从激光雷达点云中检测树木并测量胸径，算法可在数据采集的同时运行，也可事后在已记录的数据集上运行。此外，得益于所提出的快速特征提取与变换估计模块，已记录数据输入SLAM的频率可高于常规SLAM算法。研究通过手持传感器平台及林业移动机器人采集的实地数据测试了该方案的性能，并将结果精度与当前最先进的SLAM方案进行了对比。
英文摘要：
Forests, as critical components of our ecosystem, demand effective monitoring and management. However, conducting real-time forest inventory in large-scale and GNSS-interrupted forest environments has long been a formidable challenge. In this paper, we present a novel solution that leverages robotics and sensor-fusion technologies to overcome these challenges and enable real-time forest inventory with higher accuracy and efficiency. The proposed solution consists of a new SLAM algorithm to create an accurate 3D map of large-scale forest stands with detailed estimation about the number of trees and the corresponding DBH, solely with the consecutive scans of a 3D lidar and an imu. This method utilized a hierarchical unsupervised clustering algorithm to detect the trees and measure the DBH from the lidar point cloud. The algorithm can run simultaneously as the data is being recorded or afterwards on the recorded dataset. Furthermore, due to the proposed fast feature extraction and transform estimation modules, the recorded data can be fed to the SLAM with higher frequency than common SLAM algorithms. The performance of the proposed solution was tested through filed data collection with hand-held sensor platform as well as a mobile forestry robot. The accuracy of the results was also compared to the state-of-the-art SLAM solutions.
