Context-Aware Sensor Adaption of a Radar and Time-of-Flight Based Perception Platform

Josef Steinbaeck, Andreas Strasser, Christian Steger, Eugen Brenner, Gerald Holweg, Norbert Druml

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


This paper presents an approach to enhance the perception quality of a multi-sensor system by considering the context information. The platform's context state is obtained by combining data from the perception sensors and from additional context sensors. This context information is then utilized to dynamically adapt the sensing/processing parameters to the current context. Additionally, the system is capable to detect a reduced perception performance of individual sensors caused by environmental influences.The proposed approach was implemented on a multi-sensor platform, equipped with Time-of-Flight (ToF) cameras, radar sensors and multiple context sensors. The static Robot Operating System (ROS) architecture of the existing platform was extended to support automatic parameter adaption during runtime. In order to demonstrate the approach with real-world data, the platform was exposed to different scenarios. The proposed approach results in a significantly increased perception quality compared to the output of a static implementation.

Original languageEnglish
Title of host publication2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781728148427
Publication statusPublished - Mar 2020
Event15th IEEE Sensors Applications Symposium: SAS 2020 - Kuala Lumpur, Malaysia
Duration: 9 Mar 202011 Mar 2020

Publication series

Name2020 IEEE Sensors Applications Symposium, SAS 2020 - Proceedings


Conference15th IEEE Sensors Applications Symposium
Abbreviated titleSAS 2020
CityKuala Lumpur


  • context-aware
  • radar
  • ROS
  • self-adaptive
  • sensor fusion
  • time-of- flight

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Instrumentation


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