CORRESPONDENCE DETERMINATION AND RELATIVE ORIENTATION OF POINT CLOUDS ACQUIRED BY TERRESTRIAL LASER SYSTEM

• Grant number: 23/14756-1
• Support Opportunities: Scholarships in Brazil - Master
• Start date: July 01, 2024
• End date: February 28, 2025
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geodesy
• Principal Investigator: Mauricio Galo
• Grantee: Matheus Ferreira da Silva
• Host Institution: Faculdade de Ciências e Tecnologia (FCT). Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Presidente Prudente , SP, Brazil
• Associated research grant: 21/06029-7 - High resolution remote sensing for digital agriculture, AP.TEM

Abstract

The use of high spatial resolution remote sensors for precise crop mapping has become increasingly relevant in precision agriculture, supporting sustainable and highly profitable production models. LiDAR technology has gained prominence in agricultural management studies due to its potential to identify object structures with a high level of detail. In this context, system calibration and registration of point clouds are essential to ensuring the quality of the acquired LiDAR data. Point cloud registration is a stage which allows to obtain the complete representation of the environment by matching scans acquired from different positions. In this project, it is proposed an automatic approach for relative orientation of multiple scans acquired by a terrestrial LASER scanner (TLS) system in agricultural areas, contributing with accurate crop mapping. In general, the proposed approach has two potential contributions: i) the use of natural objects as primitives, such as the center of mass of trunks and the geometric center of fruit, not requiring the distribution of man-made targets; ii) the use of relaxation labeling concept to establish neighborhood relationships and object matching, reducing ambiguities due to the presence of repetitive patterns found in agricultural crops. The results will be evaluated qualitatively and quantitatively through visual inspection and geometric accuracy measurements. Other metrics could be used to assess the efficiency of the algorithm, scalability, and robustness within the context of this project.