Visual-inertial UAV attitude estimation using urban scene regularities

We present a drift-free attitude estimation method that uses image line segments for the correction of accumulated errors in integrated gyro rates when an unmanned aerial vehicle (UAV) operates in urban areas. Since man-made environments generally exhibit strong regularity in structure, a set of line segments that are either parallel or orthogonal to the gravitational direction can provide visual measurements for the absolute attitude from a calibrated camera. Line segments are robustly classified with the assumption that a single vertical vanishing point or multiple horizontal vanishing points exist. In the fusion with gyro angles, we introduce a new Kalman update step that directly uses line segments rather than vanishing points. The simulation and experiment based on urban images at distant views are provided to demonstrate that our method can serve as a robust visual attitude sensor for aerial robot navigation.