Abstract

RRxIO offers robust and accurate state estimation even in challenging visual conditions. RRxIO combines radar ego velocity estimates and Visual Inertial Odometry (VIO) or Thermal Inertial Odometry (TIO) in a single filter by extending rovio. Thus, state estimation in challenging visual conditions (e.g. darkness, direct sunlight, fog) or challenging thermal conditions (e.g. temperature gradient poor environments or outages caused by non uniformity corrections) is possible. In addition, the drift free radar ego velocity estimates reduce scale errors and the overall accuracy as compared to monocular VIO/TIO. RRxIO runs many times faster than real-time on an Intel NUC i7 and achieves real-time on an UpCore embedded computer.

We wsih to explore the following possibilities:

• can the radar repalce the LiDAR in the GNSS/LIDAR/IMU integration?
• how can the radar help with the GNSS?
• is it possible to explore the multiple IMUs?

Dr. Wen, W. and Yang Peiwen.

Recent Research Plan: Peiwen Yang (Huawei ZGD2+ “PolyU 3+1” under application)

• [11/10/2022] Discussion on the research topics
  • replicate the radar/inertial odometry
  • survey the challenges of the event camera
  • how to certify that the calibration results of the imu/odometer is certifibly correct?
  • the integration of multiple IMU?
• [11/10/2022] 0.7 year research plan (by March 2023)-IEEE ITSC conference
  • Test the autonomous driving vehicle
  • Help to supervise FYP students, Edward