基于下视相机和IMU的单应初始化与动态加权算法
Homography Initialization and Dynamic Weighting Algorithm Based on a Downward-Looking Camera and IMU
AI总结：
针对下视相机MAV的VIO初始化失败和累积漂移问题，提出基于单应的初始化方法及动态加权视觉残差策略，在真实数据集上验证了成功初始化和定位精度提升。
摘要：
近年来，视觉惯性里程计（VIO）技术已相当成熟，并被广泛应用于许多场景。然而，将VIO应用于配备下视相机的微型飞行器（MAV）时，我们仍面临挑战。具体而言，VIO在起飞过程中无法计算正确的初始化结果，且当MAV在空中飞行时累积漂移较大。为克服这些问题，我们提出一种基于单应的初始化方法，该方法利用下视相机在起飞期间检测到的特征近似位于同一平面这一事实。随后，我们引入先验法向量和运动场以提高状态精度。此外，为处理累积漂移，我们提出一种动态加权视觉残差的策略。最后，我们在采集的真实世界数据集上评估了我们的方法。结果表明，无论MAV如何起飞，我们的系统都能成功初始化，且定位误差也得到大幅改善。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
