Continuous close-range 3D object pose estimation

TL;DR

This paper introduces a real-time 3D object pose estimation method using a gradient-ascend particle filter that effectively handles close-range partial views, enhancing flexibility in manufacturing automation.

Contribution

The novel approach models potential views in full 6D space and applies online during tasks, improving robustness and speed over traditional view-based methods.

Findings

Converges to correct pose within 10-15 iterations

Achieves average accuracy of less than 8mm

Demonstrated on a real assembly task

Abstract

In the context of future manufacturing lines, removing fixtures will be a fundamental step to increase the flexibility of autonomous systems in assembly and logistic operations. Vision-based 3D pose estimation is a necessity to accurately handle objects that might not be placed at fixed positions during the robot task execution. Industrial tasks bring multiple challenges for the robust pose estimation of objects such as difficult object properties, tight cycle times and constraints on camera views. In particular, when interacting with objects, we have to work with close-range partial views of objects that pose a new challenge for typical view-based pose estimation methods. In this paper, we present a 3D pose estimation method based on a gradient-ascend particle filter that integrates new observations on-the-fly to improve the pose estimate. Thereby, we can apply this method online during task execution to save valuable cycle time. In contrast to other view-based pose estimation methods, we model potential views in full 6- dimensional space that allows us to cope with close-range partial objects views. We demonstrate the approach on a real assembly task, in which the algorithm usually converges to the correct pose within 10-15 iterations with an average accuracy of less than 8mm.