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.

翻译：相对于一组感兴趣物体的精确定位使移动机器人能够执行多种任务。随着能够部署深度神经网络（DNN）进行实时推理的边缘设备的兴起，利用人工智能（AI）从原始图像数据中提取特定于物体的语义信息（如物体类别和相对六自由度位姿）是合理的。然而，在扩展卡尔曼滤波器（EKF）中融合此类基于AI的测量，需要量化DNN的不确定性和离群值剔除能力。本文阐述了在基于AI的物体相对状态估计中重构测量方程的优势。通过推导使用直接物体相对位姿测量的EKF，我们可以解耦位置和旋转测量，从而限制错误旋转测量的影响，并允许部分测量剔除。此外，我们研究了用DNN预测的随机不确定性替换6-DoF物体相对位姿测量的固定测量协方差矩阵，为状态估计器带来的性能和一致性改进。