Development of an adaptive TDCP and RTK/INS tightly coupled navigation system for autonomous vehicles
Bibliographic record
Abstract
Abstract. Autonomous vehicle technologies are useful for unmanned ground vehicles, mobile robotics, micro-air vehicles, and logistics which become one of the key research points in recent years. Real-Time Kinematic/Inertial Navigation System (RTK/INS) tightly coupled systems are widely used in navigation systems. They use complementary information of Global Navigation Satellite System (GNSS) and INS to provide continuous and robust positioning and navigation solutions in various application scenarios. In this paper, we extend time-differenced carrier phase (TDCP) in RTK/INS tightly coupled algorithm to achieve low-power consumption navigation system, which can aid autonomous vehicles getting high accuracy position. In conventional RTK/INS tightly coupled systems, the pseudorange, Doppler, and carrier phase of GNSS are used to integrate with INS complementarily, which has high power consumption. Because the sampling rate of Inertial Measurement Unit (IMU) is usually around hundreds of hertz and the RTK/INS tightly coupled algorithm is complicated. Unlike conventional RTK/INS tightly coupled system, this system adaptively utilizes TDCP positioning module to work independently at lower sample rates and simple structure getting high-precision position in some good condition like open-sky. RTK/INS tightly coupled module will stop work at this time to save power consumption and computation. In addition, considering the positioning error of TDCP will drifting, this system will adaptively use RTK/INS tightly coupled module to correct the drifting error of TDCP periodically to help system maintain high-precision navigation continuously. Experimental results show the positioning error of TDCP remained within relatively acceptable bounds for general navigation scenarios despite the drift. The maximum errors over 30 minutes in east, north, and up direction are around 0.333 m, −0.446 m, and 3.598 m, respectively. Besides, RMS significantly decreases with calibration by RTK/INS tightly coupled system compared to cases without calibration, which demonstrates the effectiveness of periodic correction in mitigating cumulative drift for TDCP.
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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".