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Visual odometry based on structural matching of local invariant features using stereo camera sensor.

Pedro Núñez¹, Ricardo Vázquez-Martín, Antonio Bandera

¹Departamento de Tecnología de los Computadores y las Comunicaciones, University of Extremadura, Escuela Politécnica, Avda. Universidad s/n, 10071 Cáceres, Spain. pnuntru@unex.es

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

This summary is machine-generated.

This study introduces a novel visual odometry system for stereo cameras, estimating motion using image features. The efficient method enhances mobile robot navigation in real-world environments.

Keywords:

combined constraint matching algorithm maximum-weighted clique robotic stereo vision sensor visual odometry sensor

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Area of Science:

Robotics
Computer Vision
Sensor Systems

Background:

Accurate motion estimation is crucial for autonomous systems like mobile robots.
Existing visual odometry methods face challenges in real-time performance and accuracy.

Purpose of the Study:

To develop a novel sensor system for estimating stereo camera motion using visual odometry.
To improve the efficiency and accuracy of motion estimation for mobile robot navigation.

Main Methods:

Utilizes local invariant image features matched between stereo pairs and consecutive frames.
Employs a fast matching algorithm combining absolute and relative feature constraints.
Represents scene view and 3D features as graphs, finding maximum-weighted cliques for consistent matches.

Main Results:

The system achieves real-time visual odometry by linking image features into trajectories.
Minimizing geometric and algebraic errors enhances the accuracy of stereo camera displacement estimation.
Experimental results validate the approach's performance in real environments for mobile robotics.

Conclusions:

The proposed visual odometry system offers an efficient and accurate method for stereo camera motion estimation.
This technology has significant potential for applications in industrial and service robotics.
The novel matching algorithm combining feature constraints improves robustness and performance.