Texture-Aware SLAM Using Stereo Imagery and Inertial Information
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
We present a gaze control method that augments an existing stereo and inertial Simultaneous Localization And Mapping (SLAM) system by directing the stereo camera towards feature-rich regions of the scene. Our integrated active SLAM system is based on careful triangulation of visual features, existing successful nonlinear optimization, and visual loop closing frameworks. It relies on the tight coupling of IMU measurements with constraints imposed by visual correspondences from both stereo and motion. Alongside the SLAM system, the gaze control module also runs in real-time and includes an efficient online classifier that segments the scene into texture classes and assigns a quality score to each class that correlates with the availability of reliable features for tracking. Based on this quality score, the gaze selection module controls a pan-tilt unit that directs the camera to focus on high-reward texture classes. We validate our system in both indoor and outdoor spaces, and we show that active gaze control crucially improves the robustness and long-term operation of the localization 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