A Reliability-Aware Visual-Inertial Odometry for Dynamic and Low-Texture Environments

Abstract: Visual-inertial odometry (VIO) tends to degrade in aggressive dynamic and low-texture environments, where rapid motion, weak visual structure, and moving objects reduce the reliability of visual observations. This study presents TRAIL-VIO, a temporal reliability-aware visual-inertial odometry framework with line feature enhancement. The method estimates temporal observation reliability by combining semantic priors with IMU-based motion consistency, which allows a continuous and time-varying assessment of observation quality instead of frame-wise decisions. A reliability-aware point-line association scheme is also introduced, where inertial prediction is used to constrain feature matching and partially corrupted line segments are selectively retained. In addition, a reliability-guided marginalization strategy is applied to reduce the influence of unreliable visual constraints before they are incorporated into the prior. Experiments on the EuRoC MAV benchmark and a self-collected UAV dataset show that TRAIL-VIO achieves average RMSE values of 0.042 m and 9.51 m, respectively, outperforming representative baseline methods in dynamic and low-texture scenarios. Additional ablation, parameter-sensitivity, and runtime analysis further verify the contribution of the main modules, the robustness of the selected parameters, and the computational feasibility of the proposed framework.

Yelu Liu, Ruokun Qu, Mengcheng Xu, Chenglong Li and Hui Jiang. “A Reliability-Aware Visual-Inertial Odometry for Dynamic and Low-Texture Environments”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170593

@article{Liu2026,
title = {A Reliability-Aware Visual-Inertial Odometry for Dynamic and Low-Texture Environments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170593},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170593},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Yelu Liu and Ruokun Qu and Mengcheng Xu and Chenglong Li and Hui Jiang}
}