POINT CLOUD REGISTRATION USING LASER DATA FROM AN ORANGE ORCHARD

Abstract

Mobile laser systems have been used for close-range applications, such as agricultural management, due to the high penetrability of the laser beam enabling a unique 3D representation of the plant structure. However, some challenges remain in generating 3D mapping in agricultural environments. For instance, orange orchards are composed of trees with dense canopies which the laser scanner cannot penetrate for stem mapping. Therefore, most of the laser point clouds are pulses reflected from leaves and ground. This paper analyses approaches to detect keypoints, extract features and register the point clouds acquired with a laser scanner in an orange orchard. Three keypoints extraction methods (Uniform Sampling - US, 3D Harris and 3D Intrinsic Shape Signatures – 3D ISS), SHOT feature extraction and global registration methods (ICP and CPD) were evaluated. The results showed that improvements are still necessary for point cloud registration for orange orchards. In feature-based matching methods, an approach to filter the mismatches is needed to improve the estimation of the translation and rotation parameters. In the global registration methods, initial values of the trajectory are needed for ICP. CPD achieved good results for five sequence scans without initial values.