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In recent years, the technology in visual-inertial odometry (VIO) has maturedconsiderably and has been widely used in many applications. However, we stillencounter challenges when applying VIO to a micro air vehicle (MAV) equippedwith a downward-looking camera. Specifically, VIO cannot compute the correctinitialization results during take-off and the cumulative drift is large whenthe MAV is flying in the air. To overcome these problems, we propose ahomographybased initialization method, which utilizes the fact that thefeatures detected by the downward-looking camera during take-off areapproximately on the same plane. Then we introduce the prior normal vector andmotion field to make states more accurate. In addition, to deal with thecumulative drift, a strategy for dynamically weighting visual residuals isproposed. Finally, we evaluate our method on the collected real-world datasets.The results demonstrate that our system can be successfully initialized nomatter how the MAV takes off and the positioning errors are also greatlyimproved.

Homography Initialization and Dynamic Weighting Algorithm Based on a Downward-Looking Camera and IMU