From Timing Variations to Performance Degradation: Understanding and Mitigating the Impact of Software Execution Timing in SLAM

Abstract

Timing is an important property for robotic systems that continuously interact with our physical world. Variation in program execution time caused by limited computational resources or system resource contention can lead to significant impact on algorithmic result accuracy. Even though recent work has found Simultaneous Localization And Mapping (SLAM) to be timing-sensitive, little exists in understanding the interactions between the timing variations in SLAM systems and the corresponding degradation. In this paper we conduct a systematic analysis of nine state-of-the-art SLAM systems and dissect the root causes of their degradation. We discovered that timing-induced errors are generated either from delayed execution in certain critical tasks, or from desynchronization in sensor fusion. Based on the insights from our analysis, we propose a solution that combines selective fusion on data in the front end and temporal budget optimization on bundle adjust-ment in the backend to mitigate the impacts of unexpected timing variation adaptively. Experimental results show that our proposed method makes it possible to migrate expensive algorithms to low-cost platforms without laborious tuning, while making the SLAM system robust against the effects of abnormal timing.