An Efficient Visual SLAM Framework for Autonomous Systems on Resource-Constrained Platforms
Bibliographic record
Abstract
One of the primary technologies for autonomous navigation is Simultaneous Localization and Mapping. It allows a vehicle or robot to create a map of its environment while simultaneously understanding where it is on the map. Visual SLAM provides so by using data from LiDAR, cameras, and other optical sensors. With their efficiency, many of SLAM techniques require costly equipment like RGB-D cameras and laser scanners, as well as high processing power and strong GPUs. Their application in transportable or low-cost systems is limited by these limitations. This paper suggests a real-time, lightweight visual SLAM method that can operate on small devices like the Raspberry Pi. the method reduces the need for costly depth sensors by calculating depth information through differences in estimation using stereo cameras with successful algorithms. ORB and FLANN are used for feature detection and tracking, enabling accurate localization while maintaining computational speed. The suggested method provides an available solution for robotics, drones and mobile platforms by achieving continuous operation on embedded systems. This study demonstrates the possibility of cheap and sustainable SLAM designs, expanding the use of autonomous navigation for small-scale robotic and consumer use cases.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".