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Streamlined Acquisition of Large Sensor Data for Autonomous Mobile Robots to Enable Efficient Creation and Analysis of Datasets

Mark Niemeyer; Julian Arkenau; Sebastian Pütz; Joachim Hertzberg
In: 2024 IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2024), May 13-17, Yokohama, Japan, IEEE, 2024.


The increasing utilization of modern AI techniques represents a transforming shift in the robotics domain. Training and accessing new models requires substantial amounts of application-specific data, but the limited resources onboard mobile robots (like processing power, network bandwidth, etc.) pose a challenge for the development of efficient data recording and provisioning pipelines. Furthermore, accessing specific information based on a combination of spatial, temporal and semantic information is generally not supported by currently available tools. In this paper, we present a methodology which allows the efficient recording of robotic sensor streams. We show that our approach reduces the overall time needed until the data can be served via the spatio-temporal-semantic query interface of the semantic environment representation SEEREP. We further present that the maximum sensor data rate which can be stored to disk in real-time is increased for large robotic data types like images and point clouds in comparison to frequently employed solutions within the ROS ecosystem.