RPV-SLAM: Range-augmented Panoramic Visual SLAM for Mobile Mapping System with Panoramic Camera and Tilted LiDAR
Why this work is in the frame
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Bibliographic record
Abstract
A LiDAR-assisted panoramic visual simultaneous localization and mapping (SLAM) system for a mobile mapping system (MMS) is presented in this paper. The feasibility research on the SLAM for MMS with a panoramic camera and a tilted LiDAR without GPS/IMU sparked our interest. Because of the significant disparity in spatial sensing coverage, we show that employing a panoramic camera as a primary sensor for SLAM is more suitable than using a tilted LiDAR in this particular sensor combination. Existing panoramic visual SLAM systems, on the other hand, produce up-to-scale results, making them inappropriate for many applications that require metrically-scaled results. We develop a panoramic visual SLAM system that uses LiDAR points to generate metrically-scaled outputs to address this constraint. First, the suggested SLAM system augments visual features with ranges generated from LiDAR points. Following that, the visual features are fed into the SLAM pipeline, which performs tracking, local mapping, and loop closing. Finally, the scale information in the ranges augmented to visual features is integrated into the SLAM pipeline via the production of metrically-scaled map points, eventually leading to metrically-scaled SLAM results. Extensive testing in challenging outdoor conditions has proven the effectiveness and robustness of the proposed SLAM system.
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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