90 likes | 98 Vues
Pose Estimation for non-cooperative Spacecraft Rendevous using CNN. Ryan McKennon-Kelly.
E N D
Pose Estimation for non-cooperative Spacecraft Rendevous using CNN Ryan McKennon-Kelly Sharma, Sumant, Connor Beierle, and Simone D’Amico. “Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Convolutional Neural Networks,” September 19, 2018. https://arxiv.org/abs/1809.07238.
Pose Estimation via Monocular Vision • Motivation: • On-board determination of pose (i.e. relative position and attitude) of target spacecraft is highly enabling for future on-orbit servicing missions • Monocular systems (e.g. single camera) are inexpensive, often of low SWAP (size, weight, and power), and therefore are easily integrated into spacecraft • Current approaches depend on classical image processing techniques to identify features • This often requires them to be ‘hand engineered’ • Critical Issues: • Robustness to adverse illumination conditions • Scarcity of datasets required for training and benchmarking • E.g. may further be infeasible on-board due to computational complexity of traditional algorithms’ evaluation of a large number of pose hypotheses
Methods • Problem: Need to determine the attitude, position of Camera Reference C wrt the body frame of the target B • Proposed solution: Train a CNN for this problem • Challenge: Manual collection and labeling of large amounts of space imagery is extremely difficult • Approach: • Create a pipeline for automated generation and labeling of synthetic space imagery • Leverage Transfer Learning which pre-trains CNN on existing dataset (ImageNet) • Develop and explore several (5) separate networks against 7x separate datasets
“Nothing’s Perfect” – Comparison of High vs. Low Confidence Solutions High Confidence Solutions Low Confidence Solutions
Conclusions • Size of training set correlates with accuracy • Warrants generation of larger synthetic data sets by varying the target location and orientation • Classification networks trained with some noise did better on test images with even high gaussian “white noise” • Proves that as long as the sensor noise can be modeled or removed via post-processing, the CNN has good potential for success in real-world application • All networks trained using transfer learning – which only required training the last few layers • Proves several low-level features of spaceborne imagery are also present in terrestrial objects • Also proves there is no need to train a network completely from scratch • Accuracy of the network trained on the largest dataset (75k images w/3000 pose labels) higher than classical feature detection algorithms (3x “high confidence” solutions) • Shows promise of methods • Several caveats representing potential for future development: • Networks need to be tested with on-orbit imagery • Larger dataset required for comprehensive comparative of CNN-based pose determination vs. conventional techniques • Performance w/other spacecraft or orbit regimes untested • Ties of pose estimation to navigation performance needs to be explored