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

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.

Automated summary

The study introduces an efficient 3D registration algorithm based on ICP for eye-in-hand configuration using an RGB-D camera, with faster GPU implementation and high resolution. Results indicate that the point-to-plane linear least squares optimizer provides the best performance in terms of accuracy and efficiency.