Impact Analysis of Algorithm Optimization on Robot Deep Learning Localization Model for Real-Time Execution
Bibliographic record
Abstract
The increased demand for autonomous robots in industries such as healthcare, manufacturing, and logistics is driven by the need to address labor shortages and enhance operational efficiency. A critical aspect of these robots is their ability to navigate complex environments autonomously, which relies heavily on multiple artificial intelligence models, including visual odometry. Visual odometry enables robots to estimate their motion by analyzing visual data, making it essential for navigation and obstacle avoidance. However, ensuring such models operate within real-time constraints is paramount for the robot’s responsiveness, safety, and overall functionality. The complexity and depth of such a model, which utilizes camera images and other sensor data, make it challenging to achieve real-time performance. Thus, this paper thoroughly evaluates the performance of a deep-learning visual odometry model, with a focus on its execution time and computational overhead. Furthermore, it proposes an optimized implementation to meet the stringent real-time requirements for safe and efficient robot operation. The optimization is achieved through algorithm improvement to preserve the model structure and accuracy. Multiple hardware platforms were utilized to demonstrate the resource differences between edge, fog, and cloud deployments, thereby validating the effect of the proposed model optimization. The resource usage is also compared to see the impact of the modification on multiple aspects, including computation, memory, temperature, and energy. The resulting model operationalization improves the execution time while highlighting limitations for some hardware configurations.
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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 | 0.001 | 0.001 |
| 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".