Viewpoint Planning for Robot Photogrammetry Based on Initial Pose Estimation via Deep Learning

Aiming at the problem that offline planning of robot photogrammetry is affected by the initial pose calibration, a viewpoint planning method of robot photogrammetry system incorporating initial pose estimation is proposed. First, we construct a YOLO (you only look once)-based deep learning network to estimate the 3D bounding box of the measured object, and utilize the PNP (perspective-N-point) algorithm to quickly solve the object pose; Second, we randomly generate non-singular and collision-free viewpoints. Based on the 2D-3D forward and inverse mapping of camera imaging, we calculate the target visibility matrix under each perspective according to the depth principle; Finally, the entropy-weighted method is introduced, the optimization model is established with the goal of minimizing the reconstruction information entropy afterward the robot path is planned based on the TSP (travel-ling salesman problem) model. The results show that the translation error estimated via deep learning is less than 5 mm, and the angular error is less than 2°. The viewpoint planning method considering entropy weight improves the quality of photogrammetry. Simultaneously, the reconstruction speed is increased. It obtains excellent pose estimation and reconstruction results when utilizing the algorithm to verify the photogrammetric quality and efficiency of more typical parts. The proposed algorithm is extendable to practical engineering applications, especially for rapid sparse photogrammetry, improving the speed and automation of industrial photogrammetry. © 2023 Science Press. All rights reserved.