Visual-LiDAR Simultaneous Localization and Mapping
Bibliographic record
Abstract
Simultaneous Localization And Mapping (SLAM) has garnered significant attention in robotics research over the years. While SLAM has demonstrated success, its application in mobile mapping systems (MMS) presents unique challenges. This study builds upon prior research (RPV-SLAM), extending its framework to enhance accuracy and perform boundary tests on a specific MMS, Maverick MMS. Our contribution introduces a novel SLAM approach termed HDPV-SLAM, addressing critical limitations encountered by the existing system. The first challenge addressed is the sparsity of LiDAR depth data, complicating its correlation with extracted visual features from RGB images. The second challenge stems from the lack of horizontal overlap between the panoramic camera and the tilted LiDAR sensor, causing difficulties in depth association. Furthermore, a comprehensive dataset named YUTO MMS is presented to the public. This dataset spans 18.95 km and was collected from diverse environments, including York University's Keel campus and Teledyne Optech headquarters building.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".