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Related Experiment Video

Updated: Jan 10, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

A Likelihood-Based Pose Estimation Method for Robotic Arm Repeatability Measurement Using Monocular Vision.

Peng Zhang¹, Jiatian Li¹, Jiayin Liu¹

¹Faculty of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

This study introduces a new likelihood-based pose estimation method to improve robotic arm repeatability accuracy. The approach achieves high precision, outperforming existing monocular vision techniques.

Keywords:

Cramér–Rao bound Iterative Closest Point (ICP)maximum likelihood estimation pose estimation repeatability

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Area of Science:

Robotics
Computer Vision
Metrology

Background:

Repeatability accuracy is crucial for robotic arm performance.
Existing monocular vision methods have limitations in precise pose estimation.

Purpose of the Study:

To develop an advanced likelihood-based pose estimation method for enhanced robotic arm repeatability accuracy.
To overcome the limitations of current monocular vision techniques.

Main Methods:

Optimized likelihood estimation for initial pose estimation.
Iterative depth refinement and statistical modeling of observed poses.
Backprojection of 2D points to 3D and Iterative Closest Point (ICP) algorithm for error computation.

Main Results:

Achieved mean repeatability positioning accuracy of 0.0128 mm with a standard deviation of 0.0038 mm.
Demonstrated superior accuracy and stability compared to two existing monocular vision methods.
Showcased average accuracy improvements of 0.79 mm and 1.06 mm, with over 85% reduction in standard deviation.

Conclusions:

The proposed likelihood-based pose estimation method significantly enhances robotic arm repeatability accuracy.
The method offers a more accurate and stable solution for monocular vision-based robotic measurements.
This advancement has implications for high-precision robotic applications.