Detection and Mapping of an Uncooperative Spinning Target under Low-light Illumination Condition

Abstract

This paper investigates the simultaneous detection and mapping problem for inspecting an unknown and uncooperative target that is spinning in space. First, we apply a new unsupervised generative adversarial network (GAN) to enhance the low contrast and poor visibility images which are captured in low-light space illumination conditions. Second, due to the captured low-resolution (LR) images of the target contain small size of key-components, so that we propose a new small object detection network that combines a GAN-based super-resolution (SR) network and a FRCNN-based detection network to locate these objects. The SR network was used to reconstruct super-resolved images from the original LR images. Third, we utilize a SLAM-based algorithm to map and estimate the pose of the spinning target based on previous image enhancement. In summary, the integrated architecture has three components: a low-light enhancement GAN, a small object detection network, and a real-time SLAM system. The experimental results show that the integrated architecture achieves better visual quality and improves the awareness of an uncooperative spinning target.