Sparse Stereo Visual Odometry with Local Non-Linear Least-Squares Optimization for Navigation of Autonomous Vehicles
Bibliographic record
Abstract
In this thesis, the author presents a Sparse Stereo Visual Odometry system for navigation of autonomous vehicles. The proposed system has the capability to estimate the camera's pose based on its surrounding environment. In contrast to other Visual Odometry systems with Bundle Adjustment optimization, the system proposed in here differs in four main aspects: (1) it utilizes both stereo frames to track features between frames; (2) it does not require a bootstrap step to initialize the algorithm; (3) it performs a local optimization at every increment frame instead of perform a windowed optimization; and (4) it consider the both stereo images inside the optimization instead of just one side of the stereo system. The system was tested on the Karlsruhe Institute of Technology (KITTI) Vision Benchmark Suit, as well as with a set of video sequences recorded with commercial stereo cameras on the roads of the city of Ottawa, Ontario.
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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 | 0.000 |
| 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 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".