面向不变视觉惯性里程计的学习式IMU偏置预测
Learned IMU Bias Prediction for Invariant Visual Inertial Odometry
AI总结：
针对将IMU偏置引入不变滤波器状态会破坏李群对称性的问题，提出用神经网络基于历史IMU序列预测偏置，结合不变MSCKF实现鲁棒视觉惯性里程计，长时间无视觉时仍有效。
摘要：
在陌生环境中运行的自主移动机器人高度依赖精准的状态估计，通常会利用视觉和惯性测量数据。近期研究表明，扩展卡尔曼滤波器的不变公式化表述利用机器人位置、速度与姿态状态的李群结构，提升了视觉惯性里程计的收敛性与鲁棒性。但惯性传感器同样需要进行测量偏置估计，而将偏置引入滤波器状态会破坏李群对称性。本文设计了一种神经网络，可通过历史惯性测量单元（IMU）的测量序列预测IMU的偏置。这使得我们能够将不变滤波器用于视觉惯性里程计，依靠学习得到的偏置预测结果，而非将偏置引入滤波器状态。我们通过实际实验证明，搭载学习式偏置预测的不变多状态约束卡尔曼滤波器（MSCKF）可实现鲁棒的视觉惯性里程计，即便视觉信息长时间不可用、系统需完全依赖IMU测量时也能正常工作。
英文摘要：
Autonomous mobile robots operating in novel environments depend critically on accurate state estimation, often utilizing visual and inertial measurements. Recent work has shown that an invariant formulation of the extended Kalman filter improves the convergence and robustness of visual-inertial odometry by utilizing the Lie group structure of a robot's position, velocity, and orientation states. However, inertial sensors also require measurement bias estimation, yet introducing the bias in the filter state breaks the Lie group symmetry. In this paper, we design a neural network to predict the bias of an inertial measurement unit (IMU) from a sequence of previous IMU measurements. This allows us to use an invariant filter for visual inertial odometry, relying on the learned bias prediction rather than introducing the bias in the filter state. We demonstrate that an invariant multi-state constraint Kalman filter (MSCKF) with learned bias predictions achieves robust visual-inertial odometry in real experiments, even when visual information is unavailable for extended periods and the system needs to rely solely on IMU measurements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
