LiDAR-Based Multi-Sensor Fusion with 3D Digital Maps for High-Precision Positioning
Bibliographic record
Abstract
This paper presents a multi-sensor positioning and navigation system that leverages cost-effective commercial-grade sensors for GNSS-challenging urban and indoor environments. The system fuses onboard motion sensor data with LiDAR point clouds registered to high-accuracy 3D digital maps to achieve sustained decimeter-level positioning. Key contributions include accurate LiDAR scans geo-referencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Real-world driving data from downtown Kingston, Ontario, Canada, and a high-accuracy 3D city geodatabase were used to examine the proposed methods’ performance and benefits. Results demonstrate the efficacy of the proposed technique, achieving accurate positioning with an average RMSE of 20cm horizontally and 13cm vertically, and a sustainable positioning sub-meter level of positioning accuracy 100% of the time. The proposed method was also able to sustain high precision positioning in such GNSS-denied environments with position errors of less than 50cm for 96.8% of the time and less than 30cm for 91% of the time. The performance achieved demonstrates that the proposed system is a practical option for the positioning and navigation of self-driving cars and has the potential for cooperative mapping and updating 3D city maps.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".