Outdoor RGB-D Mapping Using Intel-RealSense
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we investigate simultaneous localization and mapping (SLAM) for outdoor environments using Intel-RealSense D435 RGB-D camera. Unlike the indoor RGB-D SLAM, aligning data frames in outdoor scenarios is challenging because of lack of matched RGB features and associated depth values. Furthermore, the problem with partial or no depth information from the camera side is also amplified in outdoor use. We propose a method to align successive RGB-D frames that can robustly estimate transformation between the frames with a higher accuracy. Alignment between frames is computed by jointly optimizing over both appearance and shape matches. Aligned frames are bound together followed by pose-graph optimization to achieve a globally consistent map. The experiments demonstrate that our system can robustly deal with difficult data in common outdoor scenarios.
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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