Mapping and Semantic Modeling of Underground Parking Lots Using a Backpack LiDAR System
Bibliographic record
Abstract
Presented in this paper is a novel method for the mapping and semantic modeling of an underground parking lot using 3D point clouds collected by a low-cost Backpack Laser Scanning (BLS) or LiDAR system. Our method consists of two parts: a Simultaneous Localization and Mapping (SLAM) algorithm based on Sparse Point Clouds (SPC) and a semantic modeling algorithm based on a modified PointNet model. The main contributions of this paper are as follows: (1) a probability frontend framework for the alignment of point clouds using the local point cloud surface variance as the weight of registration, which modifies registration failure caused by the lack of features in sparse point clouds, (2) a robust submap-based strategy for loop closure detection and back-end optimization under sparse point clouds, and (3) a modified PointNet model for classifying the point clouds of underground parking lots into four categories: ceiling, floor, wall, others. Experimental results show that our SPC-SLAM algorithm achieves centimeter-level accuracy (0.09% trajectory error rate) after closed loop processing in a Global Navigation Satellite System (GNSS)-denied underground parking lot, and precision of 84.8% in semantic segmentation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".