Worst-Case Time Disparity Analysis of Message Synchronization in ROS

Multi-sensor data fusion is essential in autonomous systems to support accurate perception and intelligent decisions. To perform meaningful data fusion, input data from different sensors must be sampled at time points in close propinquity to each other, otherwise the result cannot accurately reflect the status of the physical environment. ROS (Robotic Operating System), a popular software framework for autonomous systems, provides message synchronization mechanisms to address the above problem, by buffering messages carrying data from different sensors and grouping those with similar timestamps. Although message synchronization is widely used in applications developed based on ROS, little knowledge is known about its actual behavior and performance, so it is hard to guarantee the quality of data fusion. In this paper, we model the message synchronization policy in ROS and formally analyze its worst-case time disparity (maximal difference among the timestamps of the messages grouped into the same output set). We conduct experiments to evaluate the precision of the proposed time disparity upper bound against the maximal observed time disparity in real execution, and compare it with the synchronization policy in Apollo Cyber RT, another popular software framework for autonomous driving systems. Experiment results show that our analysis has good precision and ROS outperforms Apollo Cyber RT in terms of both observed worst-case time disparity and the theoretical bound. 40 2022 IEEE Real-Time Systems Symposium (RTSS)