Performance Enhancement of INS/GNSS/Refreshed-SLAM Integration for Acceptable Lane-Level Navigation Accuracy
Bibliographic record
Abstract
In the near future, multi-sensor fusion will be the core component to navigate the autonomous driving platforms. This paper proposes new strategies to cope with the integration of an inertial navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LIDAR) to achieve simultaneous localization and mapping (INS/GNSS/LiDAR SLAM) especially in GNSS challenging environments where GNSS signals are blocked or contaminated with reflected signals. The proposed strategies implement a high level of integration with various information received from multiple sensors to collectively compensate for the specific drawbacks of those sensors included in the integrated system. The first strategy is to solve the divergence and drift problems of SLAM using the initial pose information from INS and the proposed refreshing process using an INS/GNSS integrated system. In addition, an updated mechanization is designed to qualify those received measurements based on cross validation of separate types of data. This mechanization is to ensure all measurements are reliable for the Extended Kalman Filter (EKF) update process. Moreover, the SLAM-derived information plays a major role to recognize the vehicle movement which assists the system to accurately apply those appropriate vehicle motion constraint models. The preliminary results presented in this study illustrate that proposed algorithm performs superior than the traditional INS/GNSS integration scheme and provides absolute navigation accuracy of 2 meters and 0.6% of distance traveled in GNSS-denied as well as 1.2 meters in GNSS-hostile environments, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".