arXiv:2210.11668
RGB-Only Reconstruction of Tabletop Scenes for Collision-Free Manipulator Control
원문 초록
We present a system for collision-free control of a robot manipulator that uses only RGB views of the world. Perceptual input of a tabletop scene is provided by multiple images of an RGB camera (without depth) that is either handheld or mounted on the robot end effector. A NeRF-like process is used to reconstruct the 3D geometry of the scene, from which the Euclidean full signed distance function (ESDF) is computed. A model predictive control algorithm is then used to control the manipulator to reach a desired pose while avoiding obstacles in the ESDF. We show results on a real dataset collected and annotated in our lab.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
PanSt3R: Multi-view Consistent Panoptic Segmentation
Geometric Prior-Guided Neural Implicit Surface Reconstruction in the Wild
Differentiable Nonlinear Model Predictive Control
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields
Self-Supervised Multimodal NeRF for Autonomous Driving
토론
이 논문과 연결된 포럼 글이 아직 없습니다.