IP3/DR - A low-cost precise and robust GNSS/INS integrated navigation system for land vehicles
Bibliographic record
Abstract
As an advanced and efficient high-precision positioning technology, Precise Point Positioning (PPP) is capable of providing precise navigation solutions globally with the Global Navigation Satellite System (GNSS) observations from a single receiver. Benefiting from maturation of the communication infrastructure, the precise products such as satellite orbit and satellite clock corrections are currently widely available with low latency, which opens doors for its applications to various emerging applications, such as self-driving cars. The rapid rise of the low-cost GNSS receivers and chipsets is pushing on the development of low-cost and robust navigation systems with strong industry demands. To meet this market demand, the Instant PPP (IP3) featuring multi-constellations, global availability, fast convergence and continuous navigation solutions (even with GNSS correction outages) is developed. IP3 can provide navigation solution with half meter (50 cm) (Circular Error Probability (CEP) 50%) accuracy in real-time with orbits, clocks and ionosphere and code bias corrections and using observations from a single frequency (SF) GNSS receiver. To mitigate the effects of GNSS signal blockages and multipath in challenging environments, IP3/DR integration is proposed in this research by combining the IP3 with low-cost inertial navigation system (INS) to provide continuous and robust navigation solutions. We will first introduce the scheme of IP3/DR system as well as the key features. Afterwards, the performance of the IP3/DR system will be verified in three different situations, namely the normal situation with open-sky and sub-urban environment, the field test with GNSS corrections outage and the field test with GNSS observations outage. The results of the above three experiments indicate the IP3/DR can provide high precision navigation solution better than 50 cm (CEP 50%) even with 5 minutes GNSS corrections outage and short-term GNSS observations outage.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".