A generalized framework for identification and geometrical feature extraction of circular and polygonal shaped prisms

Abstract This paper introduces a versatile framework crucial for robotic applications such as object manipulation, mobile robot navigation, and pole climbing. It addresses the identification of geometric shapes and dimensions of diverse objects found in varied environments. The proposed method utilizes LiDAR scanning to capture objects from different angles, generating point clouds merged through transformations and superimpositions. After filtering and slicing, intersections are isolated and projected onto a chosen datum plane. The framework employs Non-Linear Least Square fitting via Gauss Newton iterative approach, utilizing pseudo-inverse Jacobian of a hypotrochoid to approximate polygons. The algorithm consecutively fits polygon prisms, determining the best fit with the least norm of error. Results indicate an average least square error of less than 9% for radius fitting and a high f-score for shape identification.