A Robust Visual Inertial Odometry SLAM Considering Robot Self Dynamics

ABSTRACT In this paper, to deal with the dynamic SLAM problem, we investigate feature tracking and IMU preintegration in visual‐inertial odometry (VIO) and design a robust SLAM framework that explicitly considers robot self‐dynamics. We propose a self‐dynamics and IMU‐aided feature tracker to predict initial optical flow and an iterative refinement method that accounts for patch affine deformation and illumination changes, improving tracking accuracy and robustness. Furthermore, we introduce an SE 2 (3)‐based IMU preintegration that preserves state correlations and consistently encodes robot self‐dynamics for subsequent optimisation. A VIO framework with preprocessing, optimisation and loop‐closing threads is developed to validate the proposed self‐dynamics–aware tracker and SE 2 (3)‐based preintegration. Experiments, including module tests and ablation studies, demonstrate improved feature tracking accuracy, IMU noise propagation and overall VIO performance when explicitly modelling robot self‐dynamics.