SEEREP

Table of Contents

• Maintainer
• General
• Quick Start
• Documentation
• Related Publications

Maintainer

Mark Niemeyer
mark.niemeyer@dfki.de

German Research Center for Artificial Intelligence
DFKI Niedersachsen
Plan-Based Robot Control

General

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-TemporalSemantic 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.

Quick Start

To deploy the server locally the following docker run command or the following docker-compose.yml can be used.

docker run \
  --volume=seerep-data:/mnt/seerep-data \
  --publish=9090:9090 \
  --name=seerep_server \
  --tty \
  ghcr.io/agri-gaia/seerep_server:latest \
  --data-folder=/mnt/seerep-data

Example docker-compose.yml:

version: "3.9"
services:
  seerep:
    image: ghcr.io/agri-gaia/seerep_server:latest
    tty: true
    container_name: seerep_server
    ports:
      # the gRPC port
      - 9090:9090
    volumes:
      # persist the data folder
      - seerep-data:/mnt/seerep-data
    environment:
      - TZ=Europe/Berlin
      - SEEREP_DATA_FOLDER=/mnt/seerep-data
      - SEEREP_LOG_PATH=/mnt/seerep-data/log
      - SEEREP_LOG_LEVEL=info
volumes:
  seerep-data:

Documentation

The general MkDocs based documentation of this project is available here. There the general architecture is described, installation instructions are given and basic tutorials are given.

The Doxygen based code documentation can be found here.

Related Publications

@inproceedings{hoellmann2021seerep,
  author = {Höllmann, Mark and Hertzberg, Joachim},
  title = {SEEREP: towards a spatio-temporal-SEmantic Environment REPresentation for robots in an agricultural context},
  booktitle = {Perception and Navigation for Autonomous Robotics in Unstructured and Dynamic Environments Workshop on
  2021 IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year = {2021},
  month = {10}
}

@inproceedings{niemeyer2022seerep,
  author = {Niemeyer, Mark and Pütz, Sebastian and Hertzberg, Joachim},
  title = {A Spatio-Temporal-Semantic Environment Representation for Autonomous Mobile Robots equipped with
  various Sensor Systems},
  booktitle = {2022 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems},
  year = {2022},
  month = {9},
  publisher = {IEEE}
}

@inproceedings{Niemeyer2023Anchoring,
  author = {Niemeyer, Mark and Renz, Marian and Hertzberg, Joachim},
  title = {Object Anchoring for Autonomous Robots using the Spatio-Temporal-Semantic Environment Representation SEEREP},
  booktitle = {KI 2023. German Conference on Artificial Intelligence (KI-2023), 46th, September 27-29, Berlin, Germany},
  year = {2023},
  publisher = {Springer}
}