A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in dynamic scenes.
Recognizing that fusing camera with radar provides complementary information to radar, Daniel Morris, Associate Professor of Electrical and Computer Engineering, and his team developed a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, they address the association problem between radar returns and camera images with a neural network that is trained to estimate radar-camera correspondences. Experimental results on the nuScenes dataset verify the validity of the method and show significant improvements over the state-of-the-art in velocity estimation and accumulation of radar points.
A paper associated with this project was recently accepted as an oral presentation at the International Conference on Computer Vision, Oct 2021.
See more at: https://www.egr.msu.edu/3dvision/autovision.html with publication.