Lite-SVO: Towards A Lightweight Self-Supervised Semantic Visual Odometry Exploiting Multi-Feature Sharing Architecture
Wenhui Wei,Jiantao Li,Kaizhu Huang,Jiadong Li,Xin Liu,Yangfan Zhou,Wenhui Wei,Jiantao Li,Kaizhu Huang,Jiadong Li,Xin Liu,Yangfan Zhou
Not relying on ground-truth data for training, self-supervised semantic visual odometry (SVO) has recently gained considerable attention. Within self-supervised SVO, feature representation inconsistency between semantic/depth and pose tasks presents a significant challenge, as it may disrupt cross-task feature representations and lead to notable performance degradation. Regrettably, existing self-...
