Utilization of the Space Vision System as an Augmented Reality System For Mission Operations
Bibliographic record
Abstract
Augmented reality is a technique whereby computer generated images are superimposed on live images for visual enhancement. Augmented reality can also be characterized as dynamic overlays when computer generated images are registered with moving objects in a live image. This technique has been successfully implemented, with low to medium levels of registration precision, in an NRA funded project entitled, Improving Human Task Performance with Luminance Images and Dynamic Overlays. Future research is already being planned to also utilize a laboratory-based system where more extensive subject testing can be performed. However successful this might be, the problem will still be whether such a technology can be used with flight hardware. To answer this question, the Canadian Space Vision System (SVS) will be tested as an augmented reality system capable of improving human performance where the operation requires indirect viewing. This system has already been certified for flight and is currently flown on each shuttle mission for station assembly. Successful development and utilization of this system in a ground-based experiment will expand its utilization for on-orbit mission operations. Current research and development regarding the use of augmented reality technology is being simulated using ground-based equipment. This is an appropriate approach for development of symbology (graphics and annotation) optimal for human performance and for development of optimal image registration techniques. It is anticipated that this technology will become more pervasive as it matures. Because we know what and where almost everything is on ISS, this reduces the registration problem and improves the computer model of that reality, making augmented reality an attractive tool, provided we know how to use it. This is the basis for current research in this area. However, there is a missing element to this process. It is the link from this research to the current ISS video system and to flight hardware capable of utilizing this technology. This is the basis for this proposed Space Human Factors Engineering project, the determination of the display symbology within the performance limits of the Space Vision System that will objectively improve human performance. This utilization of existing flight hardware will greatly reduce the costs of implementation for flight. Besides being used onboard shuttle and space station and as a ground-based system for mission operational support, it also has great potential for science and medical training and diagnostics, remote learning, team learning, video/media conferencing, and educational outreach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".