基于视觉的不完全可见性下平均3D目标姿态的协同估计
Vision-Based Cooperative Estimation of Averaged 3D Target Pose under Imperfect Visibility
AI总结：
针对视觉传感器网络中因视角限制或遮挡导致部分相机无法观测目标的问题，提出一种扩展的网络视觉运动观测器机制，实现局部姿态估计的实时平均，并分析反馈增益与平均性能的关系。
摘要：
本文研究了视觉传感器网络中基于视觉的3D目标物体姿态协同估计。在前期工作中，我们提出了一种称为网络视觉运动观测器的估计机制，实现了局部姿态估计的实时平均。本文对该机制进行了扩展，使其即使在由于有限视角和遮挡导致部分相机无法观测目标的情况下也能工作，以充分利用网络化视觉系统的优势。然后，我们分析了所提机制实现的平均性能，并阐明了算法中反馈增益与性能之间的关系。最后，通过仿真验证了算法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
