# Indirect Object-to-Robot Pose Estimation from an External Monocular RGB Camera

arXiv: https://arxiv.org/abs/2008.11822 · HTML: https://mlsanity.com/paper/2008.11822/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Single-view robot pose and joint angle estimation via render & compare](https://mlsanity.com/paper/2104.09359/) (2021)
- [CheckerPose: Progressive Dense Keypoint Localization for Object Pose Estimation with Graph Neural Network](https://mlsanity.com/paper/2303.16874/) (2023)
- [Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps](https://mlsanity.com/paper/2207.02519/) (2022)
- [Global-Aware Monocular Semantic Scene Completion with State Space Models](https://mlsanity.com/paper/2503.06569/) (2025)
- [GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation](https://mlsanity.com/paper/2405.04890/) (2024)

## Abstract (arXiv)

We present a robotic grasping system that uses a single external monocular RGB camera as input. The object-to-robot pose is computed indirectly by combining the output of two neural networks: one that estimates the object-to-camera pose, and another that estimates the robot-to-camera pose. Both networks are trained entirely on synthetic data, relying on domain randomization to bridge the sim-to-real gap. Because the latter network performs online camera calibration, the camera can be moved freely during execution without affecting the quality of the grasp. Experimental results analyze the effect of camera placement, image resolution, and pose refinement in the context of grasping several household objects. We also present results on a new set of 28 textured household toy grocery objects, which have been selected to be accessible to other researchers. To aid reproducibility of the research, we offer 3D scanned textured models, along with pre-trained weights for pose estimation.