Multi-Sensor Fusion for Navigation of Ground Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, a navigation solution for a ground vehicle is developed by fusing navigation data from Inertial Navigation System (INS), Visual Odometry (VO), and Global Positioning System (GPS) using a Dual Extended Kalman Filter (DEKF) algorithm. The research contributions are divided in two stages. The first stage presents VO navigation system termed as Modified Stereo Visual Odometry (ModSVO) which modifies the pose estimation and pose optimization segments of the traditional stereo vision pipelines to provide an algorithm which is shown to improve accuracy when compared with the tradition Stereo Visual Odometry (SVO) approach. The second stage presents the development of INS/VO/GPS integrated navigation system using DEKF. The developed navigation solution is shown to outperform the INS/VO integrated system in case of VO failure and outperform the INS/GPS integrated system in case of GPS failure. The experimental evaluation is conducted on the well-known KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) real-world dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it