3 accepted papers
Spherical Modular Self-reconfigurable Robots (SMSRs) have been popular in recent years. Their Self-reconfigurable nature allows them to adapt to different en-vironments and tasks, and achieve what a single module could not achieve. To collaborate with each other, relative localization between each m
Room segmentation plays a significant role in scene understanding, semantic mapping, and scene coverage for robots navigating in real-world indoor environments. However, most previous works take a passive segmentation that requires a complete and uncluttered grid map as input, often resulting in low
The object navigation task requires robots to understand the semantic regularities in their environments. However, existing modular object navigation frameworks rely on instance segmentation models trained at fixed camera height viewpoints, limiting generalization performance and increasing labeling