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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".