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Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

Updated: May 26, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Two efficient solutions for visual odometry using directional correspondence.

Oleg Naroditsky¹, Xun S Zhou, Jean Gallier

¹GRASP Laboratory, Department of Computer and Information Science, University of Pennsylvania, Levine Hall North, 3330 Walnut Street, Philadelphia, PA 19104-6228, USA. narodits@cis.upenn.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 7, 2011

Summary

This summary is machine-generated.

This study introduces efficient computer vision solutions for determining camera movement using three image points and a reference direction. These methods improve visual odometry accuracy and computational efficiency for robots and mobile devices.

Related Experiment Videos

Last Updated: May 26, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Area of Science:

Computer Vision
Robotics
Geometric Deep Learning

Background:

The relative pose problem is crucial for robot navigation and 3D reconstruction.
Existing methods like the five-point algorithm have limitations in certain scenarios.
Inertial Measurement Units (IMUs) provide valuable directional information.

Purpose of the Study:

To develop novel, efficient solutions for the two-view relative pose problem using three point correspondences and a reference direction.
To introduce a new RANSAC-based visual odometry method.
To evaluate the performance of the proposed methods against established algorithms.

Main Methods:

A closed-form solution for the three-plus-one relative pose problem.
An algebraic geometry-based solution offering numerical advantages.
A RANSAC (Random Sample Consensus) algorithm for robust visual odometry computation.

Main Results:

The proposed methods provide accurate solutions for the relative pose problem.
The new visual odometry approach demonstrates superior performance in real-world experiments.
The solutions are applicable when using vanishing points or gravity vectors as reference directions.

Conclusions:

The presented solutions offer efficient and robust alternatives for relative pose estimation.
The new visual odometry method enhances navigation accuracy in robotic and mobile systems.
This work advances the state-of-the-art in computer vision for motion estimation.