Visual-Inertial Odometry Drift: Evaluating VIO in FiftyOne

FiftyOne 1.22.0 introduces the TII-RATM Drone Racing dataset, a 0.58 GB benchmark containing six indoor quadrotor racing flights recorded as native MCAP episodes with onboard visual-inertial odometry (VIO), motion-capture ground truth, fisheye video, IMU data, and feature tracks. Comparing the drone’s self-estimated position with ground truth shows that four of six flights end at their maximum recorded tracking error, suggesting their VIO drift did not recover before landing, while one ellipse flight reduced a 2.35-meter peak error to 0.67 meters by its final pose. The article proposes the final-error-to-maximum-error ratio as a complement to RMSE, since RMSE can obscure whether localization errors recover or persist; for example, the recovering flight had a lower overall RMSE than several flights that ended at their worst drift. The worst-performing flight, a lemniscate run with 1.66-meter RMSE, can be examined in FiftyOne’s multimodal viewer, which directly displays synchronized 3D trajectories, camera imagery, and tracked features from MCAP topics. A separate analysis finds that the fisheye feature tracker produced usable matches for only about 54% to 58% of camera frames across all flights, highlighting a consistent limitation likely associated with fast indoor motion, blur, and lens distortion. The evaluation approach is presented as applicable to robots, inspection drones, and other vision-based localization systems that can be compared against an independent reference such as motion capture, RTK GPS, or surveyed markers.

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