Vision-aided Inertial System for Near-real-time Positioning and Navigation of Unmanned Ground Survey Vehicles in GNSS-denied Environments
Bibliographic record
Abstract
Positioning and navigation of unmanned ground vehicles (UGVs) in GNSS-denied environments is crucial to geological surveys required for subterranean and extraterrestrial exploration and mapping. While GNSS is the most accurate and reliable source of positioning service when operating in open sky environments, many field sites of geological interest for exploration and mining cannot receive GNSS signals. The use of robotic platforms helped improve data resolution and data collection speed compared to previous survey methods. The technological advances and the low cost of micro-electro-mechanical system (MEMS)-based inertial sensors (accelerometers and gyroscopes) enabled their use in positioning and navigation for many years. However, during prolonged surveys, the inertial-based positioning solution may become prone to rapid drift resulting in substantial position errors. This research explores integrating MEMS-based inertial sensors with a vision-based navigation system. The work completed in this thesis proposes a new method for aiding the inertial-based navigation solution with the vision-based navigation solution. Each of the subsystems is modified in order to optimize the navigation solution. The vision-based navigation relies on a visual odometry pipeline modified to replace the feature matching step with Lucas-Kanade optical flow. Simultaneously, the inertial-based subsystem is derived from the three-dimensional reduced inertial sensor system (3D-RISS), modified for a body-centred update. A unique body-centred approach relying on projecting position increments through time is used to minimize the errors previously occurring due to the correlation of position increment and heading angle. The fusion of both systems is based on a Kalman filter to estimate the position, heading and inertial sensor errors. The developed method was tested using a Husky A200 UGV. The field tests utilized the Robotic Operating System (ROS) for recording data from sensors and synchronization. The test trajectories were performed indoors at the Royal Military College of Canada in Kingston, Ontario and in the Brockville Railway Tunnel in Brockville, Ontario. This thesis presents illuminating findings of aiding a body-centred inertial system with only reliable vision updates, resulting in a reliable positioning and navigation solution operating for an extended duration in GNSS-denied environments.
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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 | 0.001 |
| 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".