# Video Object Segmentation-based Visual Servo Control and Object Depth Estimation on a Mobile Robot

## TL;DR

This paper presents a novel approach combining video object segmentation with visual servo control and depth estimation on a mobile robot, enabling real-time object identification, localization, and grasping without camera calibration.

## Contribution

It introduces a segmentation-based visual servo control method and a Hadamard-Broyden update formulation for quick learning of actuator-visual feature relationships without calibration.

## Key findings

- Successfully identified and grasped objects from the YCB dataset.
- Tracked people and articulated objects in real-time.
- Validated on a mobile HSR robot with various configurations.

## Abstract

To be useful in everyday environments, robots must be able to identify and locate real-world objects. In recent years, video object segmentation has made significant progress on densely separating such objects from background in real and challenging videos. Building off of this progress, this paper addresses the problem of identifying generic objects and locating them in 3D using a mobile robot with an RGB camera. We achieve this by, first, introducing a video object segmentation-based approach to visual servo control and active perception and, second, developing a new Hadamard-Broyden update formulation. Our segmentation-based methods are simple but effective, and our update formulation lets a robot quickly learn the relationship between actuators and visual features without any camera calibration. We validate our approach in experiments by learning a variety of actuator-camera configurations on a mobile HSR robot, which subsequently identifies, locates, and grasps objects from the YCB dataset and tracks people and other dynamic articulated objects in real-time.