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

arXiv: https://arxiv.org/abs/1903.08336 · HTML: https://mlsanity.com/paper/1903.08336/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2019.

## Built on by (most cited first, 14 of 14)

- [Sa2VA: Marrying SAM2 with MLLM for Dense Grounded Understanding of Images and Videos](https://mlsanity.com/paper/2501.04001/) (2025)
- [Advancing Complex Video Object Segmentation via Progressive Concept Construction](https://mlsanity.com/paper/2507.15852/) (2025)
- [Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem](https://mlsanity.com/paper/2210.00411/) (2022)
- [Mobile Robot Manipulation using Pure Object Detection](https://mlsanity.com/paper/2201.12437/) (2022)
- [X-Prompt: Multi-modal Visual Prompt for Video Object Segmentation](https://mlsanity.com/paper/2409.19342/) (2024)
- [Depth from Camera Motion and Object Detection](https://mlsanity.com/paper/2103.01468/) (2021)
- [MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applications](https://mlsanity.com/paper/2411.19717/) (2024)
- [Frequency-Aware Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2210.05479/) (2022)
- [CLIPUNetr: Assisting Human-robot Interface for Uncalibrated Visual Servoing Control with CLIP-driven Referring Expression Segmentation](https://mlsanity.com/paper/2309.09183/) (2023)
- [Hierarchical Visual Prompt Learning for Continual Video Instance Segmentation](https://mlsanity.com/paper/2508.08612/) (2025)
- [Kinematically-Decoupled Impedance Control for Fast Object Visual Servoing and Grasping on Quadruped Manipulators](https://mlsanity.com/paper/2307.04918/) (2023)
- [ClickVOS: Click Video Object Segmentation](https://mlsanity.com/paper/2403.06130/) (2024)
- [2nd Place Report of MOSEv2 Challenge 2025: Concept Guided Video Object Segmentation via SeC](https://mlsanity.com/paper/2509.23838/) (2025)
- [SAM-MT: Real-Time Interactive Multi-Target Video Segmentation](https://mlsanity.com/paper/2607.08688/) (2026)

## Abstract (arXiv)

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.