Impact Analysis of Algorithm Optimization on Robot Deep Learning Localization Model for Real-Time Execution

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.