CNNによるクレーター検出を用いた月面地形相対航法
Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection
CNNで月面画像からクレーターを検出し、既知のクレーターと照合してEKFで宇宙機の位置を推定する地形相対航法を構築し、従来の画像処理手法より位置・速度推定誤差を大幅に低減した。
著者: Lena M. Downes, Ted J. Steiner, Jonathan P. How
分類: cs.CV, cs.RO, cs.SY, eess.IV, eess.SY
原文アブストラクト
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.
