Real-time Mapping of Multi-Floor Buildings Using Elevators
Bibliographic record
Abstract
This paper discusses the creation of a map of multi-floor buildings using elevators in autonomous and manual driving modes. Two disadvantages of existing Simultaneous Localization And Mapping (SLAM) algorithms are inability to detect elevation change and drift inside reflective environments such as many modern elevators. Therefore, we integrate the LeGO-LOAM SLAM algorithm with air pressure data collected by a barometric pressure sensor to create a map of a multi-story building in real-time without losing track of robot’s movement inside an elevator. To achieve this, we developed an elevator detection module to detect elevators using a depth camera and locate them in floor maps. In autonomous driving mode, after exploring and mapping one floor, the robot autonomously navigates to the detected elevator, takes it to another floor, and starts mapping the new floor without losing track of the robot’s position despite sudden changes in the environment during this process. The manual driving mode is subsequently added to evaluate the performance of the system and the accuracy of the generated map. The experimental results show that the proposed algorithm is capable of mapping multiple floors autonomously and manually with minimal drift.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".