Three‐dimensional SLAM for mapping planetary work site environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this paper, we present a robust framework suitable for conducting three‐dimensional simultaneous localization and mapping (3D SLAM) in a planetary work site environment. Operation in a planetary environment imposes sensing restrictions, as well as challenges due to the rugged terrain. Utilizing a laser rangefinder mounted on a rover platform, we have demonstrated an approach that is able to create globally consistent maps of natural, unstructured 3D terrain. The framework presented in this paper utilizes a sparse‐feature‐based approach and conducts data association using a combination of feature constellations and dense data. Because of feature scarcity, odometry measurements are also incorporated to provide additional information in feature‐poor regions. To maintain global consistency, these measurements are resolved using a batch alignment algorithm, which is reinforced with heterogeneous outlier rejection to improve its robustness to outliers in either measurement type (i.e., laser or odometry). Finally, a map is created from the alignment estimates and the dense data. Extensive validation of the framework is provided using data gathered at two different planetary analogue facilities, which consist of 50 and 102 3D scans, respectively. At these sites, root‐mean‐squared mapping errors of 4.3 and 8.9 cm were achieved. Relative metrics are utilized for localization accuracy and map quality, which facilitate detailed analysis of the performance, including failure modes and possible future improvements. © 2012 Wiley Periodicals, Inc.
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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