重新表述基于AI的多目标相对状态估计以用于基于概率不确定性的部分测量异常值剔除
Reformulating AI-based Multi-Object Relative State Estimation for Aleatoric Uncertainty-based Outlier Rejection of Partial Measurements
AI总结：
本文提出通过重新表述测量方程，利用DNN预测的概率不确定性来提高基于AI的多目标相对状态估计中部分测量异常值剔除的性能。
摘要：
精确地相对于一组感兴趣的物体进行定位使移动机器人能够执行各种任务。随着边缘设备能够部署深度神经网络（DNN）进行实时推断的能力提升，使用人工智能（AI）来从原始图像数据中提取物体特定的语义信息，如物体类别和相对六自由度（6-DoF）姿态，是合乎逻辑的。然而，将此类基于AI的测量融合到扩展卡尔曼滤波器（EKF）中需要量化DNN的不确定性和异常值剔除能力。本文提出了重新表述基于AI的物体相对状态估计中的测量方程的好处。通过推导使用直接物体相对姿态测量的EKF，我们可以将位置和旋转测量解耦，从而限制错误旋转测量的影响，并允许部分测量剔除。此外，我们研究了将6-DoF物体相对姿态测量的固定测量协方差矩阵替换为DNN预测的概率不确定性对状态估计器的性能和一致性改进。
英文摘要：
Precise localization with respect to a set of objects of interest enables mobile robots to perform various tasks. With the rise of edge devices capable of deploying deep neural networks (DNNs) for real-time inference, it stands to reason to use artificial intelligence (AI) for the extraction of object-specific, semantic information from raw image data, such as the object class and the relative six degrees of freedom (6-DoF) pose. However, fusing such AI-based measurements in an Extended Kalman Filter (EKF) requires quantifying the DNNs' uncertainty and outlier rejection capabilities. This paper presents the benefits of reformulating the measurement equation in AI-based, object-relative state estimation. By deriving an EKF using the direct object-relative pose measurement, we can decouple the position and rotation measurements, thus limiting the influence of erroneous rotation measurements and allowing partial measurement rejection. Furthermore, we investigate the performance and consistency improvements for state estimators provided by replacing the fixed measurement covariance matrix of the 6-DoF object-relative pose measurements with the predicted aleatoric uncertainty of the DNN.
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。
