Terrain relative navigation can improve the precision of a spacecraft’s state estimate by providing supplementary measurements to correct for drift in an inertial measurement unit. This paper presents a crater detector, LunaNet, that uses a convolutional neural network (CNN) and image processing methods to detect craters from camera imagery taken by a spacecraft’s onboard camera. These detections are matched with known lunar craters, and these matches are used as visual landmark measurements in an extended Kalman filter (EKF). Our results show that, on average, LunaNet detects approximately twice the number of craters in an intensity image as two prior intensity-image-based crater detectors, and detects more accurate craters than the other two detectors as well. One of the challenges of using cameras for this task is that they can generate imagery with significant variations in image quality and noise levels. LunaNet is robust to four common types of image noise due to its incorporation of a CNN that is trained on diverse data. LunaNet also produces crater detections with better image persistence over a trajectory. These qualities contribute to a LunaNet-based EKF that results in consistently lower state estimation error and that outperforms the filters based on the other detectors.

地形相对导航可以通过提供补充测量值来校正惯性测量单元中的漂移，从而提高航天器状态估计的精度。本文介绍了一个陨石探测器LunaNet，它使用卷积神经网络（CNN）和图像处理方法从航天器机载摄像头拍摄的摄像机图像中检测陨石坑。这些检测与已知的月球坑相匹配，并且这些匹配被用作扩展卡尔曼滤波器（EKF）中的视觉界标测量。我们的结果表明，LunaNet平均检测到强度图像中的凹坑数量大约是两个现有的基于强度图像的凹坑检测器的两倍，并且还检测到比其他两个检测器更精确的凹坑。使用相机执行此任务的挑战之一是它们会生成图像质量和噪点水平有很大差异的图像。由于LunaNet包含经过多种数据训练的CNN，因此它对四种常见的图像噪声类型均具有较强的鲁棒性。LunaNet还可以产生弹坑检测，并在轨迹上具有更好的图像持久性。这些品质有助于基于LunaNet的EKF，从而导致状态估计误差始终较低，并且优于其他检测器所基于的滤波器。