H-VLO: Hybrid LiDAR-Camera Fusion For Self-Supervised Odometry
Eren Aydemir,Naida Fetic,Mustafa Unel,Eren Aydemir,Naida Fetic,Mustafa Unel
In this paper, we propose a hybrid visual-LiDAR odometry (H-VLO) framework that fuses predicted visual depth map and completed LiDAR map. Compared to the previous visual-LiDAR odometry methods, our approach leverages 2D feature matching and 3D association by utilizing deep depth map, deep flow map and deep LiDAR depth completion networks. Rather than extraction of the depth values from LiDAR measu...
