Enhancing Visual–Inertial Odometry Robustness and Accuracy in Challenging Environments

Visual–Inertial Odometry (VIO) algorithms are widely adopted for autonomous drone navigation in GNSS-denied environments. However, conventional monocular and stereo VIO setups often lack robustness under challenging environmental conditions or during aggressive maneuvers, due to the sensitivity of visual information to lighting, texture, and motion blur. In this work, we enhance an existing open-source VIO algorithm to improve both the robustness and accuracy of the pose estimation. First, we integrate an IMU-based motion prediction module to improve feature tracking across frames, particularly during high-speed movements. Second, we extend the algorithm to support a multi-camera setup, which significantly improves tracking performance in low-texture environments. Finally, to reduce the computational complexity, we introduce an adaptive feature selection strategy that dynamically adjusts the detection thresholds according to the number of detected features. Experimental results validate the proposed approaches, demonstrating notable improvements in both accuracy and robustness across a range of challenging scenarios.