论文标题
使用卷积神经网络进行视觉火山口检测的月球地形相对导航
Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection
论文作者
论文摘要
地形相对导航可以通过检测全球特征作为补充测量的全球特征来纠正惯性导航系统漂移的全球特征，从而提高航天器的位置估计值的精度。本文提出了一个使用卷积神经网络（CNN）和图像处理方法来跟踪使用扩展卡尔曼滤波器（EKF）跟踪模拟航天器的位置的系统。 CNN称为lunanet，在视觉上检测模拟的摄像机框架中的陨石坑，并且这些检测与当前估计的航天器位置区域的已知月球陨石坑相匹配。这些匹配的陨石坑被视为使用EKF跟踪的功能。 Lunanet可以在模拟轨迹上进行更可靠的位置跟踪，这是因为其对图像亮度变化的鲁棒性以及在整个轨迹中从框架到框架的更可重复的火山口检测。与使用标准亮度图像对EKF相比，使用基于图像加工的火山口检测方法相比，Lunanet与EKF结合的平均最终位置估计误差下降了60％，平均最终速度估计误差的平均速度估计误差降低了25％。
Terrain relative navigation can improve the precision of a spacecraft's position estimate by detecting global features that act as supplementary measurements to correct for drift in the inertial navigation system. This paper presents a system that uses a convolutional neural network (CNN) and image processing methods to track the location of a simulated spacecraft with an extended Kalman filter (EKF). The CNN, called LunaNet, visually detects craters in the simulated camera frame and those detections are matched to known lunar craters in the region of the current estimated spacecraft position. These matched craters are treated as features that are tracked using the EKF. LunaNet enables more reliable position tracking over a simulated trajectory due to its greater robustness to changes in image brightness and more repeatable crater detections from frame to frame throughout a trajectory. LunaNet combined with an EKF produces a decrease of 60% in the average final position estimation error and a decrease of 25% in average final velocity estimation error compared to an EKF using an image processing-based crater detection method when tested on trajectories using images of standard brightness.
