Tightly Integrated Smartphone GNSS and Visual odometry for Enhanced Urban Pedestrian Positioning
Bibliographic record
Abstract
Precise smartphone-based positioning service is challenging in dense urban areas due to significant multipath effects in GNSS signals received by smartphone devices. The raw GNSS measurements will be contaminated by non-line-of-sight (NLOS) signals, severely deteriorating the smartphone positioning accuracy. Many methods have been proposed to mitigate the GNSS NLOS problem, including 3D mapping-aided GNSS, RAIM, and machine learning-based methods. But these methods have limitations such as the need for 3D city models or external devices, high false-alarm chances, and training processes. In this study, we have developed a new approach to improve smartphone positioning accuracy in dense urban areas by coupling the smartphone GNSS and camera sensors, which are already available in most smartphones. Wholly based on themselves, the proposed method tightly integrates GNSS pseudorange, carrier-phase and Doppler measurements, and a visual odometry (VO). The GNSS measurements undergo preprocessing, DD normal equations, and velocity estimations. The smartphone images are processed using a KLT optical flow method, where GNSS velocities are applied to estimate the coordinate rotation and scale between them based on a sliding-window least-squares scheme using Horn’s method. Importantly, a quad-tree-based outlier searching (QTOS) algorithm is applied to ensure the healthiness of estimation processes throughout the integration. The data from DD GNSS normal equations, GNSS velocities, and VO velocities are input to an FGO algorithm for final positioning estimations. A field test in the dense urban area of Calgary showed an improvement of 25% in horizontal accuracy and a reduction of velocity estimation error by 30%, where the chance of positioning outliers (> 30 m) is significantly reduced by 76%. Therefore, the proposed method provides an effective solution for precise smartphone positioning in dense urban areas without the need for external data sources or training.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".