\textbf{PLATO}: \emph{Preintegration Learning from Accurate Trajectory Observations} for Neural Inertial Odometry

2026-09-06

AI Summary

本文提出PLATO方法，通过精确轨迹观测学习IMU偏差动力学及噪声协方差，以提高神经惯性里程计在复杂环境下的运动估计精度。

Abstract

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.

Problem

• Neural Inertial Odometry
• IMU Bias
• Uncertainty
• Preintegration

Innovation

• Preintegration Learning
• Neural Ordinary Differential Equation (NODE)
• Likelihood-based Framework
• Double-Adjoint Scheme
• Memory-Efficient Optimization