AI RESEARCH
Panoramic Multimodal Semantic Occupancy Prediction for Quadruped Robots
arXiv CS.CV
•
ArXi:2603.13108v1 Announce Type: cross Panoramic imagery provides holistic 360{\deg} visual coverage for perception in quadruped robots. However, existing occupancy prediction methods are mainly designed for wheeled autonomous driving and rely heavily on RGB cues, limiting their robustness in complex environments. To bridge this gap, (1) we present PanoMMOcc, the first real-world panoramic multimodal occupancy dataset for quadruped robots, featuring four sensing modalities across diverse scenes.