强大的扫描登记用于在森林环境中使用低分辨率LiDAR传感器进行导航
Himanshu Gupta1, Henrik Andreasson1, Achim J Lilienthal1,2
1Centre for Applied Autonomous Sensor Systems, Örebro University, 702 81 Örebro, Sweden.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的方法,用于在林业中使用低分辨率LiDAR进行强大的同时定位和绘图 (SLAM) 和树木绘图. 该方法提高了导航和树木定位的准确性,无需GPS或IMU,解决了自动化森林机器的劳动力短缺问题.
科学领域:
- 机器人和自动化机器人与自动化
- 林业技术 林业技术 林业技术
- 计算机视觉 计算机视觉
背景情况:
- 由于危险的条件和劳动力短缺,自动化林业机器至关重要.
- 现有的SLAM方法在复杂的林业环境中扎,传感器数据有限.
研究的目的:
- 开发一个强大的SLAM和树木绘制方法用于林业使用低分辨率LiDAR.
- 为了提高导航准确度和树位定位,而不依赖GPS或IMU.
主要方法:
- 使用树检测进行扫描登记和用低分辨率LiDAR (16Ch, 32Ch) 和固态LiDAR进行姿势校正.
- 实现了自适应式预处理和启发式树检测.
- 在私人和公共数据集上进行评估.
主要成果:
- 实现了强大的扫描注册,优于通用算法,16Ch LiDAR的RMSE减少>3m,固态LiDAR的RMSE减少3.7m.
- 与固定半径搜索相比,树木检测率增加了13%.
- 自动树干直径估计使用4.3厘米MAE (6.5厘米RMSE).
结论:
- 拟议的方法在林业条件下提供了强大的SLAM和树木映射,仅使用低分辨率LiDAR.
- 这种方法可以提高林业机器的自动化,提高效率和安全性.
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