Enhanced Design, Optimal Tuning, and Parallel Real-Time Implementation of Monocular Visual-Inertial-GNSS Navigation System
Bibliographic record
Abstract
This Ph.D. thesis presents novel design, optimal tuning, and parallel real-time implementation of Tightly-Coupled (TC) Visual-Inertial Navigation (VIN) systems integrated with the Global Navigation Satellite System (GNSS) for autonomous vehicle applications.The Visual-Inertial-GNSS Navigation (VIGN) problem concerns estimating the position, velocity, and attitude of a mobile platform carrying only an on-board camera, an Inertial Measurement Unit (IMU), and a GNSS receiver.Although the cost and size efficiency of VIGN systems offer great commercialization potentials, achieving high accuracy, robustness, and real-time performance on embedded computing platforms are still challenging.Accuracy and robustness in VIGN systems heavily rely on two factors: first, a tightly-coupled fusion scheme that harvests the deep inter-modality correlations in the sensory data, and second, a well-tuned fusion model that sufficiently characterizes the actual behaviour of the VIGN system in practice.However, fusing multi-modal sensory data in the TC fashion scheme, as in the VIGN system, inevitably imposes a high computational burden due to the large state space and diverse volumes of visual processing, making it quite challenging to satisfying strict real-time constraints on embedded computing platforms.To address these challenges, this Ph.D. thesis proposes novel design and optimization approaches that have resulted in three main contributions.This Ph.D. research initially develops an enhanced VIN system based on Multi-State Constraint Kalman Filter (MSCKF).Further, it proposes a novel systematic design and automatic tuning framework to adjust its design parameters for optimal state estimation.Evolutionary techniques based on the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm have been employed to efficiently search in a high-
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".