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

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.