用精确轨迹训练神经惯性里程计，提升复杂环境下的运动估计精度。
\textbf{PLATO}: \emph{Preintegration Learning from Accurate Trajectory Observations} for Neural Inertial Odometry
- 基于似然函数，联合学习陀螺仪/加速度计噪声与惯性偏差动态
- 在EuRoC数据集上表现优于传统方法，水下实验也有效
- 适合需要高鲁棒性的机器人定位场景
神经惯性里程计在复杂环境中展现出强劲潜力，但纯惯性预积分对IMU偏差和不确定性仍敏感。本文提出PLATO：一种基于似然的框架，利用精确轨迹观测联合学习由神经常微分方程（NODE）建模的IMU偏差动态及陀螺仪与加速度计的噪声协方差。优化过程利用负对数似然的稀疏结构，通过前向微分计算IMU噪声参数梯度。设计了一种定制化的双伴随方案，将离散不变误差伴随与连续时间伴随结合，实现对嵌套偏差动态与IMU预积分传播的内存高效似然优化。在EuRoC数据集上的验证表明性能提升，水下机器人实验也证明其在光照间歇失效与视觉退化条件下的适用性。
原文摘要 · Abstract (English)
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.
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