LiDAR-Based Multisensor Fusion With 3-D Digital Maps for High-Precision Positioning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it