用不确定性感知方法提升机器人对物体相对位姿估计的鲁棒性
Reformulating AI-based Multi-Object Relative State Estimation for Aleatoric Uncertainty-based Outlier Rejection of Partial Measurements
- 重构测量方程，解耦旋转与位置估计
- 利用DNN的随机不确定性动态调整测量噪声，提升精度
- 适合需要高可靠性定位的自动驾驶与机器人导航场景
针对移动机器人精确感知目标物体相对位置的需求，本文提出改进基于AI的多目标相对状态估计方法。传统方法将六自由度（6-DoF）位姿作为整体测量输入到扩展卡尔曼滤波器（EKF）中，难以处理部分测量缺失或异常值。本文通过直接使用物体相对位姿测量，重构测量方程，实现位置与姿态的解耦，从而限制错误姿态测量的影响，并支持部分测量的剔除。同时，将固定测量协方差替换为深度神经网络（DNN）预测的随机不确定性（aleatoric uncertainty），显著提升了状态估计的一致性与性能。实验表明，该方法在KITTI、Oxford RobotCar等数据集上实现了更稳定的定位结果，尤其在遮挡和低纹理场景下表现更优。
原文摘要 · Abstract (English)
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.
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