Shape Reconstruction Of Unknown Tumbling Target Using Factor Graph-Based Dynamic SLAM
Why this work is in the frame
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Bibliographic record
Abstract
On Orbit Servicing (OOS) is increasingly vital in modern space missions, encompassing satellite maintenance, orbital assembly, and debris removal. Within this scope, servicing spacecraft must first inspect and characterize their target before initiation of any servicing actions. This paper introduces a navigation algorithm designed to reconstruct the shape of unknown space objects using a factor graph-based Simultaneous Localization and Mapping (SLAM) formulation, drawing on observed and identified point cloud features. The algorithm employs batch optimizations of collected measurements to facilitate real-time mapping of the target object. In addition, the space object is assumed to be tumbling which directly challenges the static environment assumption prevalent in most SLAM formulations. To address this, a dynamic SLAM formulation is leveraged, and a noisy parametric model is used to propagate the dynamic map as well as to construct the dynamic factor-graph at front-end level. Loop closures of previously seen features as they rotate and evolve with the target object are possible with the incorporation of kinematic factors. The latter’s store current available estimation of the dynamic model parameters and link consecutive landmark nodes in the factor graph thus closing the loop and enhancing the robustness and accuracy of the mapping process. Besides showing that the estimated point cloud map converges to the true map after a few iterations of batch factor graph optimizations in the static case first. The results also show that the use of a dynamic model allows to track the evolution of the map in the dynamic case and that the estimated dynamic map error is further reduced when using kinematic factors to close the loops. This method demonstrates promising prospects for equipping servicing spacecraft with advanced, modern, and robust perception intelligence for inspecting unknown target objects in orbit
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it