引用本文: 尹伯卿, 邢涛, 邢艳秋, 杨书航, 常晓晴, 丁志文. 林下单木定位视觉SLAM算法研究[J]. 武汉大学学报 ( 信息科学版).
YIN Bo-qing, XING Tao, XING Yan-qiu, YANG Shu-hang, CHANG Xiao-qing, DING Zhi-wen. Research on Visual SLAM Algorithm for Individual Tree Localization in Forest[J]. Geomatics and Information Science of Wuhan University.
 Citation: YIN Bo-qing, XING Tao, XING Yan-qiu, YANG Shu-hang, CHANG Xiao-qing, DING Zhi-wen. Research on Visual SLAM Algorithm for Individual Tree Localization in Forest[J]. Geomatics and Information Science of Wuhan University.

## Research on Visual SLAM Algorithm for Individual Tree Localization in Forest

• 摘要: 森林中立木的单木定位是林业遥感领域中的重要问题，基于单目相机的视觉SLAM算法是室外空间定位和建图的重要手段，解决了由于树木冠层遮挡导致的全球导航卫星系统GNSS信号缺失的问题，然而现有的单目视觉SLAM算法无法实现样地内立木直接定位。为解决此问题，本文基于单目视觉SLAM算法提出了Indi-tree SLAM算法。该算法通过使用图像序列进行相机位姿估计、地图尺度恢复、单木位置判断和单木位置坐标计算等过程可实现样地中立木直接定位。本研究用相机对3块边长为40 m的方形样地进行样地扫描，对本文提出的Indi-tree SLAM算法进行精度验证。实验结果表明，本文算法所计算的样地立木坐标在沿x和y轴方向均方根误差均为0.44 m，平均定位误差为6.3%。本文方法实现了样地内立木的直接定位，极大的缩短了森林结构参数测量时间，为森林资源调查提供了一种准确、高效的可行性方案。

Abstract: Objectives: Individual Tree localization is an important issue in the field of forestry remote sensing. Monocular visual SLAM algorithm is a significant tool for outdoor spatial localization and mapping, which solved the problem of inability to locate due to missing GNSS signal caused by tree canopy occlusion in forest resource survey. However, the existing monocular vision SLAM algorithm cannot achieve direct localization of standing trees in sample plots. To solve this problem, we proposed the Indi-tree SLAM algorithm based on monocular vision SLAM algorithm in this paper. Methods: First, camera pose estimation was performed using the DSO-SLAM algorithm and then the actual displacement of the camera was applied to restore the map scale. Second, according to the principle of edge detection technique in depth image, the position of standing trees in sample plots was determined. Finally, the camera coordinates and tree coordinates are converted based on the relationship between the camera and standing trees during the image acquisition process, ultimately achieving the direct positioning of standing trees within the sample plots. Results: In this experiment, three square sample plots with a side length of 40 m were scanned by the camera to verify the accuracy of the Indi-tree SLAM algorithm proposed in the paper. The experimental results indicated that the root-mean-square error of the sample wood coordinates calculated by the proposed single-wood positioning algorithm is 0.44 m along both x and y axes, and the average positioning error is 6.3%. Conclusion:This study implemented direct positioning of standing trees in the sample plots, greatly reducing the measurement time of forest structure parameters and offering an accurate and efficient feasible solution for forest resource inventory.

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