点云
均方误差
激光雷达
算法
计算机科学
树(集合论)
近似误差
遥感
数学
测距
滤波器(信号处理)
预处理器
点(几何)
同时定位和映射
人工智能
统计
计算机视觉
地理
几何学
移动机器人
数学分析
电信
机器人
作者
Shuhang Yang,Yanqiu Xing,Dejun Wang,Hangyu Deng
出处
期刊:Remote Sensing
[Multidisciplinary Digital Publishing Institute]
日期:2024-07-24
卷期号:16 (15): 2714-2714
被引量:2
摘要
To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas.
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