Enhancing Autonomous Robot Safety through Localization Performance Analysis

Autonomous robots play a vital role in various applications, including industrial automation and logistics. Guaranteeing their safe and dependable operation is of utmost importance, particularly in human-robot collaborative scenarios. While existing safety standards address numerous aspects of autonomous systems, the persisting challenges linked to localization issues demand attention. Accurate localization is paramount for safety in autonomous robotic systems due to its direct impact on preventing accidents, mitigating risks, and ensuring human well-being. In scenarios where robots operate alongside humans, such as collaborative workspaces or shared environments, precise localization is crucial to avoid collisions and potential harm to individuals. Furthermore, robust localization enhances system dependability by minimizing errors and ensuring consistent performance, vital for industrial automation and logistics where disruptions can lead to costly downtime. To proactively manage these risks and ensure uninterrupted operations, this paper introduces a novel solution: the Localization Performance Analyzer (LPA). We present an architecture for estimating localization performance and a set of innovative features that serve as predictors of localization quality. Our findings indicate that implementing the LPA algorithm in a fleet of robots leads to more accurate estimations of localization performance. Our results from ablation studies demonstrate that integrating the LPA algorithm into a fleet of robots significantly enhances the accuracy of localization performance estimation. The predictor model and trainer is available at https://github.com/ulassbin/localization performance analyser.