Mobile robots require comprehensive scene understanding to operate effectively in diverse environments, enriched with contextual information such as layouts, objects, and their relationships. Although advances like neural radiance fields (NeRFs) offer high-fidelity 3D reconstructions, they are computationally intensive and often lack efficient representations of traversable spaces essential for pl...Show More
Visual Odometry (VO) empowers robots with the ability to perform self-localization within unknown environments using visual cues, yet it is faced with challenges in dynamic environments. In this study, we propose a novel monocular visual odometry network called Spatiotemporal Dual-stream Network (STDN-VO) with two parallel streams, i.e. spatial stream and temporal stream, to model spatiotemporal c...Show More
The automation of substation equipment inspection is a pivotal development area within the power industry. Traditional substation equipment inspection methods utilizing instance segmentation models trained on specific dataset have shown broad application, however, their generalization performance is limited to specific scenes. To enhance the robustness against intricate environments, we propose a ...Show More
Automatic underground parking has attracted considerable attention as the scope of autonomous driving expands. The auto-vehicle is supposed to obtain the environmental information, track its location, and build a reliable map of the scenario. Mainstream solutions consist of well-trained neural networks and simultaneous localization and mapping (SLAM) methods, which need numerous carefully labeled ...Show More
