Light at the End of the Tunnel: High-Speed LiDAR-Based Train Localization in Challenging Underground Environments
Bibliographic record
Abstract
In this paper, we present an infrastructure-free mapping and localization framework for rail vehicles using only a lidar sensor. Our method is designed to handle the pathological environment found in modern underground tunnels: narrow, parallel, and relatively smooth concrete walls with very little infrastructure to break up the empty spaces in the tunnel. By using an RQE-based, point-cloud alignment approach, we are able to implement a sliding-window algorithm, used for both mapping and localization. We demonstrate the proposed method with datasets gathered on a subway train travelling at high speeds (up to 70 km/h) in an underground tunnel for a total of 20km across 6 runs. Our method is capable of mapping the tunnel with less than 0.6% error over the total length of the generated map. It is capable of continuously localizing, relative to the generated map, to within 10cm in stations and at crossovers, and 1.8m in pathological sections of tunnel. This method improves railbased localization in a tunnel, which can be used to increase capacity on existing railways and for automated trains.
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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) | 0.000 | 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) | 0.001 | 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".