Call for Extended Abstract

It is well recognized that robust perception in adverse weathers is challenges. For instance, how to design a robust vision or LiDAR based SLAM system in extreme weather conditions (e.g., heavy snowfall) is still an open research problem. On the other hand, radar sensing has been proven to be reliable and effective in extreme weathers. We have also seen that radar sensors are increasingly adopted for advanced driver-assistance systems (ADAS) in the automotive industry. However, there are still numerous open research problems on how to effectively design, develop and deploy radar perceptual systems for long-term robot autonomy in all weather conditions.

The main objective of this workshop is to create a platform for researchers, from both academia and includes, to explore fundamental research and applications on radar perception for all-weather robot autonomy. It aims to foster discussions and exchanges, ideally creating a long-term flourishing communication channel in the community.

Important Dates

  • April 30th Submission deadline

  • May 15th Notification to authors

  • May 31st Workshop spotlight talk

Guideline for Authors

The extended abstract must fit into 2 pages, including paper materials and references. Papers exceeding the 2-page limit at the time of submission will be returned without review. Please use the official ICRA template.

Please submit via email to Sen Wang <s.wang@hw.ac.uk> and Ayoung Kim <ayoungk@kaist.ac.kr>.
The submission deadline is April 30th 23:59 (PST).

Topics

Topics include the following but not limited to

  • Radar-based localization, mapping, and SLAM;

  • Radar sensor data association;

  • Radar object detection / tracking;

  • ADAS multimodal system on radar;

  • Scene understanding (e.g., object recognition, semantic segmentation) from radar sensing;

  • Planning and behaviour prediction using radar sensing;

  • Machine/deep learning applications using radar;

  • Radar applications to robotics, autonomous vehicles, underground mining;