Enhanced Reliability of Mobile Robots with Sensor Data Estimation at Edge

The proliferation of sensing equipment serving an expansive range of applications has led the Internet of Things (loT) paradigm to cover technologies beyond Wireless Sensor Networks (WSN). Extensive advancement in electronics, communication methods and sensors has made it possible to leverage advanced technologies such as Machine Learning and Probabilistic Modeling in resource-constrained embedded systems. These techniques increase reliability and enhance interactions among physical elements of an loT-based system in which data loss or corruption seems inevitable. However, traditional data estimation and reconstruction methods cannot be directly applied considering the computational limitations at the edge of the network. Therefore, mobile robots would greatly benefit from a resource efficient sensor data recovery procedure, capable of generating near-accurate estimates at the resource-constrained Edge layer. In this paper, we introduce a novel Bayesian filtering-based data reconstruction scheme, with real-time performance and precision for incoming semantic and geometric information from a varied set of sensors to increase reliability of autonomous navigation of mobile robots. Afterwards, we corrupt each stream of observations to validate model performance against a baseline. Furthermore, we also provide benchmark on execution latency, CPU usage and current draw while running the models in a practical setup.