Monocular based 3D depth estimation and SLAM integration
Bibliographic record
Abstract
In various practical scenarios, autonomous vehicles must navigate through unfamiliar areas to reach their destinations. This navigation is facilitated by two-dimensional (2D) and three-dimensional (3D) maps. Simultaneous localization and mapping (SLAM) systems enable autonomous vehicles to map their surroundings while in motion. Traditionally, SLAM systems rely on physical sensors like LiDAR to measure distances. However, these sensors are costly and consume significant power, particularly when used with drones. Consequently, the use of monocular cameras for depth estimation of surrounding objects has gained considerable interest from both academia and industry. In this study, we integrate a recently developed deep learning monocular depth estimation model into the ORB-SLAM2 system. The integrated system has been tested by estimating trajectories and constructing 3D point cloud maps of unknown areas. In addition, preliminary experiments were conducted using a live drone. These experiments demonstrated the ability of the proposed system to produce more accurate point-cloud maps which improve the trajectory errors by 34-54% compared to contemporary approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".