Utilizing Inpainting for Keypoint Detection for Vision-Based Control of Robotic Manipulators

TL;DR

This paper introduces a markerless visual servoing method for robotic manipulators using inpainting to generate training data and real-time occlusion handling, enabling robust, model-free, vision-based control.

Contribution

It presents a novel inpainting-based data generation pipeline and a real-time occlusion inpainting model for natural feature detection in robot control.

Findings

Successful control under full visibility and partial occlusion

Eliminates need for camera calibration and robot models

Uses UKF for stable keypoint tracking

Abstract

In this paper we present a novel visual servoing framework to control a robotic manipulator in the configuration space by using purely natural visual features. Our goal is to develop methods that can robustly detect and track natural features or keypoints on robotic manipulators that would be used for vision-based control, especially for scenarios where placing external markers on the robot is not feasible or preferred at runtime. For the model training process of our data driven approach, we create a data collection pipeline where we attach ArUco markers along the robot's body, label their centers as keypoints, and then utilize an inpainting method to remove the markers and reconstruct the occluded regions. By doing so, we generate natural (markerless) robot images that are automatically labeled with the marker locations. These images are used to train a keypoint detection algorithm, which is used to control the robot configuration using natural features of the robot. Unlike the prior methods that rely on accurate camera calibration and robot models for labeling training images, our approach eliminates these dependencies through inpainting. To achieve robust keypoint detection even in the presence of occlusion, we introduce a second inpainting model, this time to utilize during runtime, that reconstructs occluded regions of the robot in real time, enabling continuous keypoint detection. To further enhance the consistency and robustness of keypoint predictions, we integrate an Unscented Kalman Filter (UKF) that refines the keypoint estimates over time, adding to stable and reliable control performance. We obtained successful control results with this model-free and purely vision-based control strategy, utilizing natural robot features in the runtime, both under full visibility and partial occlusion.