通过改进遮挡处理实现自监督单目视觉无人机模型辨识
Self-Supervised Monocular Visual Drone Model Identification through Improved Occlusion Handling
AI总结：
提出一种自监督学习方案，仅用机载单目视频和飞行控制器数据训练神经无人机模型，通过改进遮挡处理降低里程计误差15%，并集成至ROVIO系统提升高速避障飞行精度。
摘要：
在GPS拒止环境中，自运动估计对无人机至关重要。当飞行速度增加且近距离物体导致具有明显运动模糊和大量遮挡的困难视觉条件时，基于视觉的方法会面临挑战。为解决此问题，通常利用将无人机模型与惯性测量相结合的状态估计滤波器来辅助视觉。然而，这些无人机模型目前是以监督方式利用来自外部动作捕捉系统的真值数据学习的，限制了对不同环境和无人机的可扩展性。在本研究中，我们提出一种自监督学习方案，仅使用机载单目视频和飞行控制器数据（IMU和电机反馈）来训练基于神经网络的无人机模型。我们通过首先训练一个自监督相对位姿估计模型来实现这一点，该模型随后作为无人机模型的教师。为使其能在靠近障碍物的高速情况下工作，我们提出一种改进的遮挡处理方法，用于训练自监督位姿估计模型。由于该方法，所得里程计估计的均方根误差平均降低了15%。此外，学生神经无人机模型能够从机载数据中成功获取。与其教师（基于自监督视觉的模型）相比，它在更高速度下甚至变得更加准确。我们通过将神经无人机模型集成到传统的基于滤波器的VIO系统（ROVIO）中，证明了其价值，在靠近障碍物的激进3D竞速轨迹上实现了更优的里程计精度。自运动估计的自监督学习代表着在弥合于受控且昂贵的实验室环境中飞行与真实世界无人机应用之间的差距方面迈出了重要一步。视觉与无人机模型的融合将使任何环境中的任何无人机实现更高速度的飞行并改善状态估计。
英文摘要：
Ego-motion estimation is vital for drones when flying in GPS-denied environments. Vision-based methods struggle when flight speed increases and close-by objects lead to difficult visual conditions with considerable motion blur and large occlusions. To tackle this, vision is typically complemented by state estimation filters that combine a drone model with inertial measurements. However, these drone models are currently learned in a supervised manner with ground-truth data from external motion capture systems, limiting scalability to different environments and drones. In this work, we propose a self-supervised learning scheme to train a neural-network-based drone model using only onboard monocular video and flight controller data (IMU and motor feedback). We achieve this by first training a self-supervised relative pose estimation model, which then serves as a teacher for the drone model. To allow this to work at high speed close to obstacles, we propose an improved occlusion handling method for training self-supervised pose estimation models. Due to this method, the root mean squared error of resulting odometry estimates is reduced by an average of 15%. Moreover, the student neural drone model can be successfully obtained from the onboard data. It even becomes more accurate at higher speeds compared to its teacher, the self-supervised vision-based model. We demonstrate the value of the neural drone model by integrating it into a traditional filter-based VIO system (ROVIO), resulting in superior odometry accuracy on aggressive 3D racing trajectories near obstacles. Self-supervised learning of ego-motion estimation represents a significant step toward bridging the gap between flying in controlled, expensive lab environments and real-world drone applications. The fusion of vision and drone models will enable higher-speed flight and improve state estimation, on any drone in any environment.
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。
