双预积分用于相对状态估计
Dual Preintegration for Relative State Estimation
AI总结：
本文提出双预积分方法，通过整合两个平台的IMU预积分，提升相对状态估计的精度和鲁棒性，适用于VR控制器跟踪应用。
摘要：
相对状态估计在两个进行六自由度运动的移动代理之间进行相互定位。基于圆周运动原理，估计精度对参考平台的非线性旋转敏感，特别是在平台间距离较大的情况下。这种现象即使在线性化动力学中也很明显，因为累积的线性化误差会显著降低精度。在虚拟现实（VR）应用中，这表现为在头戴式显示器快速旋转时6-DoF控制器跟踪中的显著位置误差。线性化误差会引入估计中的漂移，并使估计器不一致。在陀螺仪领域，惯性测量单元（IMU）预积分被提出作为动力学观测，以实现高效的再线性化，从而减轻线性化误差。基于这一理论，我们提出了双预积分，一种新的观测方法，整合了来自两个平台的IMU预积分。该方法为连续的相对状态提供动力学约束，并支持高效的再线性化。我们还对状态进行了可观测性分析，并分析性地制定了相应的零空间。算法评估涵盖了仿真和现实世界实验。多个非线性旋转在参考平台上进行仿真，以比较所提出方法的精度与其他最先进的（SOTA）算法的精度。实地测试从偏置可观测性、非线性旋转和背景纹理的角度比较所提出的方法和SOTA算法在VR控制器跟踪应用中的表现。结果表明，所提出的方法比SOTA算法更精确和稳健。
英文摘要：
Relative State Estimation perform mutually localization between two mobile agents undergoing six-degree-of-freedom motion. Based on the principle of circular motion, the estimation accuracy is sensitive to nonlinear rotations of the reference platform, particularly under large inter-platform distances. This phenomenon is even obvious for linearized kinematics, because cumulative linearization errors significantly degrade precision. In virtual reality (VR) applications, this manifests as substantial positional errors in 6-DoF controller tracking during rapid rotations of the head-mounted display. The linearization errors introduce drift in the estimate and render the estimator inconsistent. In the field of odometry, IMU preintegration is proposed as a kinematic observation to enable efficient relinearization, thus mitigate linearized error. Building on this theory, we propose dual preintegration, a novel observation integrating IMU preintegration from both platforms. This method serves as kinematic constraints for consecutive relative state and supports efficient relinearization. We also perform observability analysis of the state and analytically formulate the accordingly null space. Algorithm evaluation encompasses both simulations and real-world experiments. Multiple nonlinear rotations on the reference platform are simulated to compare the precision of the proposed method with that of other state-of-the-art (SOTA) algorithms. The field test compares the proposed method and SOTA algorithms in the application of VR controller tracking from the perspectives of bias observability, nonlinear rotation, and background texture. The results demonstrate that the proposed method is more precise and robust than the SOTA algorithms.
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