Sparse sampling-based view planning for complex geometries

Abstract

In this paper, an automatic sampling-based view planning algorithm is proposed, for accurate 3D reconstruction of complex geometry parts present in manufacturing. The initial viewpoint sampling method is able to lower the complexity of the algorithm by creating a sparse visibility bipartite graph relating the targeted surface patches, with the potential viewpoints (camera poses defined in SE(3)), which are contained in the surroundings of the object. This graph is used to sample and simulate a subset of viewpoints, employing an iterative greedy parallel set cover which weights the coverage of the sparse and simulated visibility. This method prematurely rejects poor candidates and prioritize the viewpoints providing an increased coverage, with no expensive preprocessing of the 3D models. A randomized Greedy Heuristic with local search has been proposed to maximize the coverage, while minimizing the total inspection time, first with the set cover of the simulated viewpoints, and secondly with the sequencing of the viewpoints and robot positioning with obstacle avoidance. Furthermore, the performance of the system is demonstrated on a set of complex benchmark models from the Stanford and MIT repositories, yielding a higher coverage with a lower computational runtime compared to existing sampling-based methods. The validation of the full system has been carried scanning a Stanford Dragon positioned with a 12 axes kinematic chain composed by two robots.