A Forest Point Cloud Real-Time Reconstruction Method with Single-Line Lidar Based on Visual–IMU Fusion

Abstract

In order to accurately obtain tree growth information from a forest at low cost, this paper proposes a forest point cloud real-time reconstruction method with a single-line lidar based on visual–IMU fusion. We build a collection device based on a monocular camera, inertial measurement unit (IMU), and single-line lidar. Firstly, pose information is obtained using the nonlinear optimization real-time location method. Then, lidar data are projected to the world coordinates and interpolated to form a dense spatial point cloud. Finally, an incremental iterative point cloud loopback detection algorithm based on visual key frames is utilized to optimize the global point cloud and further improve precision. Experiments are conducted in a real forest. Compared with a reconstruction based on the Kalman filter, the root mean square error of the point cloud map decreases by 4.65%, and the time of each frame is 903 μs; therefore, the proposed method can realize real-time scene reconstruction in large-scale forests.