Camera-to-Robot Pose Estimation from a Single Image (1911.09231v4)

Abstract: We present an approach for estimating the pose of an external camera with respect to a robot using a single RGB image of the robot. The image is processed by a deep neural network to detect 2D projections of keypoints (such as joints) associated with the robot. The network is trained entirely on simulated data using domain randomization to bridge the reality gap. Perspective-n-point (PnP) is then used to recover the camera extrinsics, assuming that the camera intrinsics and joint configuration of the robot manipulator are known. Unlike classic hand-eye calibration systems, our method does not require an off-line calibration step. Rather, it is capable of computing the camera extrinsics from a single frame, thus opening the possibility of on-line calibration. We show experimental results for three different robots and camera sensors, demonstrating that our approach is able to achieve accuracy with a single frame that is comparable to that of classic off-line hand-eye calibration using multiple frames. With additional frames from a static pose, accuracy improves even further. Code, datasets, and pretrained models for three widely-used robot manipulators are made available.

• The paper presents DREAM, a novel approach that estimates camera-to-robot pose from a single image using synthetic, domain-randomized data.
• It employs an encoder-decoder network for 2D keypoint detection and the PnP algorithm to compute camera extrinsics without offline calibration.
• Empirical evaluations across various robots show comparable accuracy to multi-frame methods, enhancing calibration flexibility in dynamic environments.

Overview of Camera-to-Robot Pose Estimation from a Single Image

The paper presents a novel approach for estimating the pose of an external camera relative to a robot using only a single RGB image. This method, named DREAM (Deep Robot-to-camera Extrinsics for Articulated Manipulators), leverages a deep neural network to identify 2D projections of keypoints associated with the robot. A key innovation of this approach is its reliance solely on synthetic data for neural network training, employing domain randomization to close the gap between simulated and real-world conditions.

Methodology

The proposed system operates in two primary stages:

1. Keypoint Detection: The technique utilizes an encoder-decoder neural network architecture trained on synthetic, domain-randomized images. This network outputs belief maps corresponding to keypoints such as robot joints, facilitating their detection in a 2D space.
2. Pose Estimation: With the detected keypoints, known camera intrinsics, and joint configuration of the robot, the system applies the Perspective-n-Point (P$n$P) algorithm to compute camera extrinsics. Notably, this approach does not necessitate a traditional, cumbersome offline calibration step, potentially enabling online calibration through real-time single-frame processing.

Experimental Evaluation

Empirical evaluations across different robots (e.g., Franka Panda, Kuka LBR, and Rethink Baxter) and camera sensors demonstrate that the proposed method achieves accuracy comparable to traditional multi-frame hand-eye calibration methods, even with a singular image. The approach offers significant flexibility by removing the need for fiducial markers and shows improvements in accuracy with multiple frames, utilizing only a single camera placed arbitrarily around the robot.

Significance and Implications

The practical implications of this research are substantial in robotics operations, especially in scenarios involving dynamic environments where camera positioning can change. It obviates manual calibration, which can be particularly useful for applications requiring frequent adjustments or repositions of the camera, such as automated assembly lines, mobile robotics, or unstructured field operations.

From a theoretical perspective, this paper reinforces the potential of synthetic data combined with domain randomization to bridge real-world applicability in robotic keypoint detection and calibration tasks. The reliance solely on synthetic data for network training poses significant efficiency advantages, circumventing the expensive and time-consuming processes associated with real-world data collection and annotation.

Future Directions

While the paper showcases promising results, further research could explore integrating temporal filtering to smooth pose estimates over multiple frames, thus bolstering robustness against noise. Moreover, establishing measures of uncertainty in the estimated poses could provide additional operational safety and reliability. Extending the work to more generalized scenarios and diverse robotic environments will strengthen its applicability.

This approach could catalyze advancements in adaptive calibration methods for robotics, with potential applications in augmented reality systems, automated navigation, and enhanced human-robot interactions, reflecting the growing synergy between AI-driven techniques and robotic autonomy.