STATE ESTIMATION FOR AN UNDERWATER ROBOT USING VISUAL AND INERTIAL CUES
Bibliographic record
Abstract
This thesis addresses the problem of 3D position and orientation (pose) estimation using measurements from a monocular camera and an inertial measurement unit (IMU).While the algorithmic formulation of the problem is generic enough to be applied to any intelligent agent that moves in 3D and possesses the sensor modalities mentioned above, our implementation of the solution is particularly targeted to robots operating in underwater environments.The algorithmic approach used in this work is based on statistical estimators, and in particular the extended Kalman filter (EKF) formulation, which combines measurements from the camera and the IMU into a unique position and orientation estimate, relative to the starting pose of the robot.Aside from estimating the relative 3D trajectory of the robot, the algorithm estimates the 3D structure of the environment.We present implementation trade-offs that affect estimation accuracy versus real-time operation of the system, and we also present an error analysis that describes how errors induced from any component of the system affect the remaining parts.To validate the approach we present extensive experimental results, both in simulation and in datasets of real-world underwater environments accompanied by ground truth, which confirm that this is a viable approach in terms of accuracy.My immediate family, Tasim, Adriana and my awesome sister Vjosana have always supported and encouraged me.Without my parents' bravery to immigrate to Canada at an age when people seek stability and stillness, most of the opportunities my sister and I enjoy today would have been impossible.Some of the people who have influenced me the most happened to be teachers and professors who loved what they were doing.
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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".