用扩散模型提升单目相机对机器人位姿估计的鲁棒性

We propose MonoSE(3)-Diffusion, a monocular SE(3) diffusion framework that formulates markerless, image-based robot pose estimation as a conditional denoising diffusion process. The framework consists of two processes: a visibility-constrained diffusion process for diverse pose augmentation and a timestep-aware reverse process for progressive pose refinement. The diffusion process progressively perturbs ground-truth poses to noisy transformations for training a pose denoising network. Importantly, we integrate visibility constraints into the process, ensuring the transformations remain within the camera field of view. Compared to the fixed-scale perturbations used in current methods, the diffusion process generates in-view and diverse training poses, thereby improving the network generalization capability. Furthermore, the reverse process iteratively predicts the poses by the denoising network and refines pose estimates by sampling from the diffusion posterior of current timestep, following a scheduled coarse-to-fine procedure. Moreover, the timestep indicates the transformation scales, which guide the denoising network to achieve more accurate pose predictions. The reverse process demonstrates higher robustness than direct prediction, benefiting from its timestep-aware refinement scheme. Our approach demonstrates improvements across two benchmarks (DREAM and RoboKeyGen), achieving a notable AUC of 66.75 on the most challenging dataset, representing a 32.3% gain over the state-of-the-art.