The Robust Semantic SLAM System for Texture-Less Underground Parking Lot
Bibliographic record
Abstract
Automatic valet parking (AVP) is the autonomous driving function that may take the lead in mass production. AVP is usually needed in an underground parking lot, where the light is dim, the parking space is narrow, and the GPS signal is denied. The traditional visual-based simultaneous location and mapping (SLAM) algorithm suffers from localization loss because of inaccurate mapping results. A new robust semantic SLAM system is designed mainly for the dynamic low-texture underground parking lot to solve the problem mentioned. In this system, a 16-channel Lidar is used to help the visual system build an accurate semantic map. Four fisheye cameras mounted at the front, back, left, and right of the vehicle are also used to produce the bird’s eye view picture of the vehicle by joint calibration. The vehicle can localize itself and navigate to the target parking lot with the semantic segmented picture and the preobtained semantic map. Based on the experiment result, the proposed AVP-SLAM solution is robust in the underground parking lot.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".