LiDAR/Visual SLAM-Aided Vehicular Inertial Navigation System for GNSS-Denied Environments
Bibliographic record
Abstract
Most navigation systems in GNSS-challenged environments rely on GNSS/INS integrated navigation system, with the INS potentially providing reliable positioning during short GNSS outages. However, in the event of a prolonged GNSS signal outage, the performance of the system will be solely dependent on the INS solution, which can lead to significant drift over time. As a result, adding more onboard sensors is crucial to mitigate the limitation the GNSS/INS systems, and thereby increase the robustness of the navigation system. This study proposes a loosely-coupled (LC) integration between the INS, LiDAR simultaneous localization and mapping (SLAM), and visual SLAM using an extended Kalman filter (EKF). The developed integrated navigation system is tested on the residential and highway drive segments of the raw KITTI dataset, which simulates various driving outdoor environments in terms of feature density and driving speed. In both cases, a complete artificial GNSS outage is enforced. The results show that the proposed INS/LiDAR/visual SLAM integrated system drastically outperforms the use of INS only. The proposed integrated navigation system yielded an average reduction in the root-mean-square error (RMSE) of approximately 95%, 87%, and 53%, in the east, north, and up directions, respectively. Finally, the proposed algorithm outperformed considered state-of-the-art LiDAR SLAM algorithms.
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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 | 0.001 |
| 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".