LiDAR-Based Multisensor Fusion With 3-D Digital Maps for High-Precision Positioning
Bibliographic record
Abstract
Accurate and reliable positioning is essential for Vehicular Internet of Things (IoT) applications, such as autonomous and connected vehicles, to ensure their effective and safe operation. This calls for innovative methods that leverage various sensors and systems to fulfill such demands across diverse environmental and operational conditions. This article presents a multisensor positioning and navigation system that leverages cost-effective commercial-grade sensors for global navigation satellite system (GNSS)-challenging urban and indoor environments. The system integrates the vehicle’s onboard motion sensors (OBMSs) measurements with 3-D point clouds from light detection and ranging (LiDAR) registered to high-accuracy 3-D digital maps for sustained decimeter-level positioning accuracy. Key contributions include accurate LiDAR scan georeferencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Road test experiments on a professional land vehicle setup equipped with a multisensory navigation instrument were performed in downtown and covered parking garage environments with accurate 3-D geodatabase (GDB) available. Results from several road test trajectories demonstrate robust high-precision positioning performance with an average root mean-square error of 20 cm horizontally and 13 cm vertically, as well as position errors of less than 50 cm for 97% of the time and less than 30 cm for 90.7% of the time. 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 3-D 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".