Path Planning of Autonomous 3-D Scanning and Reconstruction for Robotic Multi-Model Perception System

Abstract

Applying a three-dimensional (3-D) reconstruction from mapping-oriented offline modeling to intelligent agent-oriented environment understanding and real-world environment construction oriented to agent autonomous behavior has important research and application value. Using a scanner to scan objects is a common way to obtain a 3-D model. However, the existing scanning methods rely heavily on manual work, fail to meet efficiency requirements, and are not sufficiently compatible with scanning objects of different sizes. In this article, we propose a creative visual coverage path planning approach for the robotic multi-model perception system (RMMP) in a 3-D environment under photogrammetric constraints. To realize the 3-D scanning of real scenes automatically, we designed a new robotic multi-model perception system. To reduce the influence of image distortion and resolution loss in 3-D reconstruction, we set scanner-to-scene projective geometric constraints. To optimize the scanning efficiency, we proposed a novel path planning method under photogrammetric and kinematics constraints. Under the RMMP system, a constraints-satisfied coverage path could be generated, and the 3-D reconstruction from the images collected along the way was carried out. In this way, the autonomous planning of the pose of the end scanner in scanning tasks was effectively solved. Experimental results show that the RMMP-based 3-D visual coverage method can improve the efficiency and quality in 3-D reconstruction.