HumanoidPano：ヒューマノイドロボットのための球面パノラマ-LiDARハイブリッドクロスモーダル知覚
HumanoidPano: Hybrid Spherical Panoramic-LiDAR Cross-Modal Perception for Humanoid Robots
パノラマ視覚とLiDARを球面ビジョントランスフォーマーで融合し、ヒューマノイドの自己遮蔽と狭視野を克服する360度BEV知覚フレームワークを提案。
著者: Qiang Zhang, Zhang Zhang, Wei Cui, Jingkai Sun, Jiahang Cao, Yijie Guo, Gang Han, Wen Zhao, Jiaxu Wang, Chenghao Sun, Lingfeng Zhang, Hao Cheng, Yujie Chen, Lin Wang, Jian Tang, Renjing Xu
分類: cs.RO
原文アブストラクト
The perceptual system design for humanoid robots poses unique challenges due to inherent structural constraints that cause severe self-occlusion and limited field-of-view (FOV). We present HumanoidPano, a novel hybrid cross-modal perception framework that synergistically integrates panoramic vision and LiDAR sensing to overcome these limitations. Unlike conventional robot perception systems that rely on monocular cameras or standard multi-sensor configurations, our method establishes geometrically-aware modality alignment through a spherical vision transformer, enabling seamless fusion of 360 visual context with LiDAR's precise depth measurements. First, Spherical Geometry-aware Constraints (SGC) leverage panoramic camera ray properties to guide distortion-regularized sampling offsets for geometric alignment. Second, Spatial Deformable Attention (SDA) aggregates hierarchical 3D features via spherical offsets, enabling efficient 360{\deg}-to-BEV fusion with geometrically complete object representations. Third, Panoramic Augmentation (AUG) combines cross-view transformations and semantic alignment to enhance BEV-panoramic feature consistency during data augmentation. Extensive evaluations demonstrate state-of-the-art performance on the 360BEV-Matterport benchmark. Real-world deployment on humanoid platforms validates the system's capability to generate accurate BEV segmentation maps through panoramic-LiDAR co-perception, directly enabling downstream navigation tasks in complex environments. Our work establishes a new paradigm for embodied perception in humanoid robotics.
