Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection

TL;DR

This paper introduces LunaNet, a CNN-based system for lunar crater detection that enhances spacecraft navigation accuracy by improving feature detection robustness and reducing position and velocity estimation errors.

Contribution

The paper presents LunaNet, a novel CNN architecture for crater detection that outperforms traditional image processing methods in lunar terrain relative navigation.

Findings

60% reduction in position estimation error

25% reduction in velocity estimation error

More reliable crater detection across varying brightness levels

Abstract

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.