PLATO：用于神经惯性里程计的基于精确轨迹观测的预积分学习
\textbf{PLATO}: \emph{Preintegration Learning from Accurate Trajectory Observations} for Neural Inertial Odometry
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中文总结 AI 辅助
针对神经惯性里程计中预积分对IMU偏置和噪声敏感的问题，提出PLATO框架，利用精确轨迹观测联合学习NODE建模的偏置动态和噪声协方差，通过双伴随优化似然，在EuRoC和水下实验中验证了改进性能与鲁棒性。
中文摘要 AI 辅助
神经惯性里程计在具有挑战性的环境中进行运动估计方面展现出强大的潜力，然而仅依赖惯性的预积分仍然对IMU偏置和不确定性敏感。为此，本文提出了PLATO：基于精确轨迹观测的预积分学习，这是一个基于似然的框架，利用精确的轨迹观测来联合学习由神经常微分方程（NODE）建模的IMU偏置动态以及陀螺仪和加速度计的噪声协方差。优化利用了负对数似然的稀疏结构，其中IMU噪声参数梯度通过前向微分计算。一种定制的双伴随方案将离散不变误差伴随与用于偏置NODE的连续时间伴随相结合，从而在嵌套的偏置动态和IMU预积分展开上实现内存高效的似然优化。在EuRoC上的验证显示了改进的性能，水下机器人实验证明了在间歇性照明故障和视觉退化下的适用性。
英文摘要
Neural inertial odometry has demonstrated strong potential for motion estimation in challenging environments, yet inertial-only preintegration remains sensitive to IMU bias and uncertainty. To this end, this paper introduces \textbf{PLATO}:~\emph{Preintegration Learning from Accurate Trajectory Observations}, a likelihood-based framework that leverages accurate trajectory observations to jointly learn IMU bias dynamics modeled by a neural ordinary differential equation~(NODE) and gyroscope and accelerometer noise covariances. Optimization exploits the sparse structure of the negative log-likelihood, with IMU noise-parameter gradients computed by forward differentiation. A tailored double-adjoint scheme couples a discrete invariant-error adjoint with a continuous-time adjoint for the bias NODE, enabling memory-efficient likelihood optimization over the nested bias-dynamics and IMU-preintegration rollouts. Validation on EuRoC shows improved performance, and underwater robot experiments demonstrate applicability under intermittent lighting failures and visual degradation.
