Monocular based 3D depth estimation and SLAM integration
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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