Fast and accurate GPU-accelerated, high-resolution 3D registration for the robotic 3D reconstruction of compliant food objects

Abstract

If we are to develop robust robot-based automation in primary production and processing in the agriculture and ocean space sectors, we have to develop solid vision-based perception for the robots. Accurate vision-based perception requires fast 3D reconstruction of the object in order to extract the geometrical features necessary for robotic manipulation. To this end, we present an accurate, real-time and high-resolution ICP-based 3D registration algorithm for eye-in-hand configuration using an RBG-D camera. Our 3D reconstruction, via an efficient GPU implementation, is up to 33 times faster than a similar CPU implementation, and up to eight times faster than a similar library implementation, resulting in point clouds of 1 mm resolution. The comparison of our 3D reconstruction with other ICP-based baselines, through trajectories from 3D registration and reference trajectories for an eye-in-hand configuration, shows that the point-to-plane linear least squares optimizer gives the best results, both in terms of precision and performance. Our method is validated for the eye-in-hand robotic scanning and 3D reconstruction of some representative examples of food items and produce of agricultural and marine origin.