Sugarcane Field Terrain Inversion and Height Recognition Based on 3D-LiDAR and Improved RANSAC Algorithm

Abstract This study proposes a method for recognizing sugarcane field ground height based on 3D-LiDAR and an improved RANSAC algorithm, aiming to accurately extract and identify the height of sugarcane growth points from the sugarcane field environment. First, ROI region extraction and Gaussian filtering preprocessing were performed on the experimental bench data collected by LiDAR. Based on the improved RANSAC algorithm and combined with the point cloud morphology characteristics, a multi-model parallel fitting strategy was adopted to simultaneously fit both ground and sugarcane models. The terrain obscured by sugarcane leaves was reconstructed through cubic polynomial fitting. Traversing the Z-direction minimum of the sugarcane point cloud and labeling the sugarcane growth points, morphological filtering was used to remove the residual noise, and finally calculating the ground height of the sugarcane growth points. The results of indoor simulations and sugarcane field experiments show that the ground height recognition algorithm can accurately identify the boundary point between sugarcane and the ground. The average errors between terrain inversion height and actual measured height are 0.09cm and 0.1cm, respectively. This provides reliable technical support for the adaptive adjustment of sugarcane harvester header height.