1. 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,

2. Robustness to adverse illumination conditions
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

3. Proposed solution: Train a CNN for this problem
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

5. Synthetic Images Generated w/Known Pose Labels

6. Networks Developed and Experimental Results

7. High Confidence Solutions Low Confidence Solutions
“Nothing’s Perfect” – Comparison of High vs. Low Confidence Solutions
High Confidence Solutions
Low Confidence Solutions

8. Size of training set correlates with accuracy
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