Vision-Based Cooperative Estimation of Averaged 3D Target Pose under Imperfect Visibility

TL;DR

This paper extends a networked visual motion observer to estimate the average 3D pose of a target object in vision networks despite limited visibility, analyzing performance and demonstrating effectiveness through simulations.

Contribution

It introduces an improved cooperative estimation mechanism that functions under imperfect visibility conditions, enhancing previous methods.

Findings

The proposed method achieves accurate pose averaging despite occlusions.

Performance analysis reveals the relation between feedback gains and estimation accuracy.

Simulations confirm the robustness and effectiveness of the approach.

Abstract

This paper investigates vision-based cooperative estimation of a 3D target object pose for visual sensor networks. In our previous works, we presented an estimation mechanism called networked visual motion observer achieving averaging of local pose estimates in real time. This paper extends the mechanism so that it works even in the presence of cameras not viewing the target due to the limited view angles and obstructions in order to fully take advantage of the networked vision system. Then, we analyze the averaging performance attained by the proposed mechanism and clarify a relation between the feedback gains in the algorithm and the performance. Finally, we demonstrate the effectiveness of the algorithm through simulation.