Evaluating Visual Inertial Odometry Using the Windy Forest Dataset

This paper examines the effect of moving vegetation on visual-inertial-odometry performance. The evaluation was performed using a custom dataset consisting of fifteen sequences in a forest scene. The sequences are comprised of three trajectories, each with five different wind/scene motion levels — allowing the relationship between scene motion and state-estimation accuracy to be examined. The dataset was created using a "semi-synthetic" approach that combines real IMU data and synthetic images to provide increased realism while maintaining precise control over the scene. The dataset is used to assess the effect of scene motion on multiple open-source visual-inertial odometry algorithms in a variety of configurations. Additionally, the results are validated using real-world sequences containing imagery of vegetation moving in the wind. The results show that scene motion as a result of moving vegetation can worsen visual-inertial odometry performance by up to a factor of ten.