Building 2D Maps with Integrated 3D and Visual Information using Kinect Sensor
Bibliographic record
Abstract
RGB-D sensors can be used as a cost effective alternative to expensive laser scanners for small scale indoor mapping applications. Since RGB-D cameras provide both color and depth data, it is possible to fuse these two data frames in 2D mapping to exploit the advantages over laser scanners. An improved technique is to capture the navigation environment in an application for goal-oriented navigation and situation assessment using a mobile robot. The proposed technique is based on incorporating RGB images and 3D information to existing 2D Simultaneous Localization And Mapping (SLAM); which has not been clearly investigated before. By integrating visual and 3D information using an inexpensive RGB-D camera, the mobile robot is better equipped to perform tasks such as sign detection, object detection, and text understanding. The motivation for such an approach is to use 3D and visual information with reduced complexity. The performance of 2D mapping is exploited here by replacing the laser scanner with a RGB-D camera. In addition, memory usage is reduced by selecting key RGB-D frames from its sequence by comparing the number of overlapped RGB features. The proposed approach is evaluated for accuracy and consistency using experimental data gathered from a real environment.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".