In recent years, the technology in visual-inertial odometry (VIO) has matured considerably and has been widely used in many applications. However, we still encounter challenges when applying VIO to a micro air vehicle (MAV) equipped with a downward-looking camera. Specifically, VIO cannot compute the correct initialization results during take-off and the cumulative drift is large when the MAV is flying in the air. To overcome these problems, we propose a homographybased initialization method, which utilizes the fact that the features detected by the downward-looking camera during take-off are approximately on the same plane. Then we introduce the prior normal vector and motion field to make states more accurate. In addition, to deal with the cumulative drift, a strategy for dynamically weighting visual residuals is proposed. Finally, we evaluate our method on the collected real-world datasets. The results demonstrate that our system can be successfully initialized no matter how the MAV takes off and the positioning errors are also greatly improved.

翻译：近年来，视觉惯性里程计（VIO）技术已相当成熟，并广泛应用于众多场景。然而，将VIO应用于搭载下视相机的微型飞行器（MAV）时仍面临挑战：起飞阶段VIO无法计算正确的初始化结果，且MAV空中飞行时累积漂移较大。为解决这些问题，我们提出一种基于单应矩阵的初始化方法，利用起飞时下视相机检测的特征点近似位于同一平面的特性。随后引入先验法向量与运动场以提高状态估计精度。此外，针对累积漂移问题，我们提出视觉残差动态加权策略。最终，在采集的真实世界数据集上评估该方法，结果表明无论MAV以何种方式起飞，系统均能成功初始化，且定位误差得到显著改善。