Object Localization Based on a Single RGB Camera for a 4-DOF Robotic Arm

This study presents a method for constructing metric depth maps based on relative depth maps for a 4-DOF manipulator with a single RGB camera, which is attached to an end effector. To create a relative depth map of a scene, we utilize the Depth Anything V2 model along with a system of twelve ArUco markers positioned at varying distances from one another. Unlike traditional methods for determining distances to objects using ArUco markers, the proposed approach does not require a continuous presence of markers within a camera's field of view; it only necessitates measuring a distance to a few markers in an initial frame. The proposed method enables the robot to perform object localization using a relative depth map concurrently with an object search process.

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Published
2025-02-12
DOI
https://doi.org/10.1109/icara64554.2025.10977595
Primary Topic
Industrial Vision Systems and Defect Detection
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article
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article

Object Localization Based on a Single RGB Camera for a 4-DOF Robotic Arm

Elvira Chebotareva, Evgeni Magid, Niyaz Imamov, Adel Mukhamedshin
2 links
Industrial Vision Systems and Defect Detection
article

Object Localization Based on a Single RGB Camera for a 4-DOF Robotic Arm

Elvira Chebotareva, Evgeni Magid, Niyaz Imamov, Adel Mukhamedshin
article en

Abstract

This study presents a method for constructing metric depth maps based on relative depth maps for a 4-DOF manipulator with a single RGB camera, which is attached to an end effector. To create a relative depth map of a scene, we utilize the Depth Anything V2 model along with a system of twelve ArUco markers positioned at varying distances from one another. Unlike traditional methods for determining distances to objects using ArUco markers, the proposed approach does not require a continuous presence of markers within a camera's field of view; it only necessitates measuring a distance to a few markers in an initial frame. The proposed method enables the robot to perform object localization using a relative depth map concurrently with an object search process.

Kazan Federal University (RU)
Affordable and clean energy
Openalex Percentile: Top 87%
Industrial Vision Systems and Defect Detection
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