Object Pose estimation for automation of repetative tasks in cluttered environments

Abstract

The agricultural industry is undergoing a transformative shift towards efficiency and large-scale production. Manual labor is unable to meet the increasing demand, leading to the development of automation techniques. During production, objects are adjusted and moved along a line to maintain productivity. This work investigates the feasibility of developing a system that detects and determines the position and rotation of a small clipping object in a cluttered environment. To achieve this feasibility, a neural network has been trained on a custom dataset with images containing 6-D pose annotations of a clip that do not require manual annotations. The designed system also executes a validation step with a second set of sensors in the form of a stereo camera. The entire methodology has been evaluated on a test setup. The small and round properties of the clip cause a rotational error that does not fall in positional and rotational requirements set by a mechanical grabber on a robotic arm. Higher positional precision of annotation data and depth information from the stereo camera is necessary to make this methodology feasible.