A Real-Time CPU-GPU Embedded Implementation of a Tightly-Coupled Visual-Inertial Navigation System
Bibliographic record
Abstract
In autonomous navigation technologies, the Multi-State Constraint Kalman Filter (MSCKF) is one of the most accurate and robust tightly-coupled fusion frameworks for Visual-Inertial Navigation (VIN). However, the adoption of the MSCKF VIN system in real-time embedded applications depends heavily on an efficient implementation of its tangled pipeline. This work initially proposes a novel parallel multi-thread implementation of the MSCKF VIN pipeline on an embedded CPU-enabled hardware that has speeded up the per-epoch processing time of the pipeline by 41% compared to the conventional sequential implementation. The heart of the MSCKF pipeline’s visual backend is an inertially-aided 3D localization of visual feature points that are reduced to a set of nonlinear optimization problems which were conventionally solved in a serial fashion using the single-objective Gauss-Newton optimization algorithm. This work leveraged the parallel architecture of an embedded GPU and further proposes an efficient parallel implementation of a multi-objective Gauss-Newton algorithm. Integration of the proposed GPU-accelerated feature localization technique in the MSCKF parallel pipeline has resulted in 33% faster per-epoch processing time and consequently, the satisfaction of strict real-time constraints. The proposed parallel MSCKF VIN pipelines have been developed using C++ and CUDA on the NVIDIA Jetson TX2 embedded board. Experimental evaluations on a real visual-inertial odometry dataset have been provided to validate the efficacy and real-time performance enhancement of the proposed parallel implementation.
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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".