Publication
A Spatio-Temporal-Semantic Environment Representation for Autonomous Mobile Robots equipped with various Sensor Systems
Mark Niemeyer; Sebastian Pütz; Joachim Hertzberg
In: 2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI-2022), September 20-22, Cranfield, United Kingdom, IEEE, 9/2022.
Abstract
The large amount of temporally and spatially high resolution sensor data of autonomous mobile robots that have to be collected in today's systems require a structured and, above all, efficient management and storage already during the robot mission.
We present SEEREP: A Spatio-Temporal-Semantic Environment Representation for Autonomous Mobile Robots. SEEREP deals with spatial, temporal and semantic linked data at once and provides an efficient query interface for all three modalities that can be combined for high-level analyses.
It supports the most popular robotic sensor data such as images and point clouds, as well as sensor and robot coordinate frames changing over time.
Furthermore, SEEREP provides an efficient HDF5-based storage system running on the robot during operation compatible with ROS and the corresponding sensor message definitions.
The compressed HDF5 data backend can be transferred efficiently to an application server with a running SEEREP query server providing gRPC interfaces with Protobuf and Flattbuffer message types.
Partially unstructured environments that changes over time, as for example agricultural environments, can be understood based on high-level planning and reasoning systems using the SEEREP query server.
We show the superiority of SEEREP as a spatio temporal semantic environment system against a traditional GIS which cannot handle the different types of robotic sensor data.