Multi-dimensional Mapping of Confined Areas using a Hexapod Robot with Integrated Sensor Data and SLAM
Bibliographic record
Abstract
Mobile robots are increasingly used to explore and inspect confined environments, often in situations that are hazardous or inaccessible to humans, highlighting the need for advanced mapping and sensing capabilities.This paper presents a novel approach for constructing a multi-dimensional map by integrating sensor data from a hexapod robot with Simultaneous Localization and Mapping (SLAM).The robot, equipped with a DHT22 sensor and powered by a Raspberry Pi 4 Model B, was deployed in a mechanical room at the University of Alberta to collect humidity and temperature data while simultaneously mapping the environment.The Cartographer SLAM algorithm was used for mapping, and the sensor data was fused with the generated map by the Inertial Measurement Unit (IMU) using a bilinear interpolation algorithm.This pilot experiment serves as a demonstration of the proposed robot system, which results in a multidimensional map that combines 2D geographical map with sensory maps such as humidity and temperature.The map provides a visualization of the spatial distribution of environmental variables within the confined area.This approach has potential applications in various scenarios, including quick mapping of hazardous areas and routine inspections of areas with limited access.Future work will focus on incorporating 3D SLAM and exploring the use of machine learning techniques for automated anomaly detection within the multi-dimensional map, while addressing the current limitations related to real-time processing and visualization.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 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".