Comparative Analysis of SLAM Algorithms for Voice-Controlled Autonomous Wheelchair
Bibliographic record
Abstract
This paper compares various SLAM (Simultaneous Localization and Mapping) algorithms to determine the most suitable for autonomous navigation of robotic electric wheelchairs. The autonomous wheelchair can navigate, avoid obstacles, and respond to voice commands from the user, allowing it to reach its destination without needing joystick control. This feature is particularly beneficial for individuals with limited or no upper limb mobility who may struggle with joystick-operated wheelchairs. The system uses LiDAR technology to scan for nearby walls and obstacles and create a map of its surroundings. The study evaluates three leading SLAM algorithms-Hector, Gmapping, and RTAB-Map-in various scenarios with dynamic and static obstacles to identify the best algorithm for the project. The SLAM techniques utilize open-source codes from the ROS (Robot Operating System) to construct LiDAR-based maps and localization efficiently. The system is designed to be easily and safely integrated with existing electric wheelchairs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".