Processing real-time analytics of big data streams from sensor data will continue to be an important task as embedded technology increases and we continue to generate new types and ways of data analysis, particularly in regard to the internet of things. Robotics models many of these key challenges well and incorporates the possibility of high-throughput streams and complex online machine-learning and analytics algorithms. These challenges make it an almost ideal candidate for in-depth analysis of real-time streaming analytics.
Jay White Bear shares a new integrated framework on Kafka and Spark Streaming to explore a constrained SLAM problem—an ongoing research area in robotics for autonomous vehicles that is recognized as a nontrivial problem space in both industry and research—that uses online algorithms to navigate and map a space in real time. Jay presents benchmarks of the open source robot’s integration with Kafka and Spark Streaming for performance against other SLAM algorithms currently in use, outlines some of the challenges faced in the implementation, and makes recommendations for improving performance and optimization on the framework. Jay concludes by demonstrating real-time usage of the implementation with Turtlebot II and exploring relevant benchmarks and their implications on the future of autonomous vehicles in the IoT and cloud analytics space.
Jay White Bear is a data scientist and advisory software engineer at IBM. Jay holds a degree in computer science from the University of Michigan, where her work focused on databases, machine learning, computational biology, and cryptography. Jay has also done work on multiobjective optimization, computational biology, and bioinformatics at the University of California, San Francisco and machine learning, multiobjective optimization for path planning, and cryptography at McGill University.
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