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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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Implementation of Visual Odometry on Jetson Nano.

Jakub Krško¹, Dušan Nemec¹, Vojtech Šimák¹

¹Department of Control and Information Systems, FEIT, University of Zilina, 010 26 Zilina, Slovakia.

Sensors (Basel, Switzerland)

|February 26, 2025

Summary

This study implements ORB-SLAM3 for robot visual odometry on a Jetson Nano, achieving accurate localization with low-power systems. Challenges and error sources are discussed for real-world mobile robot tracking.

Keywords:

Jetson Nano ORB-SLAM3 single board computer visual SLAM visual odometry

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Area of Science:

Robotics
Computer Vision
Embedded Systems

Background:

Visual odometry is crucial for robot navigation.
Implementing advanced algorithms on low-power systems presents unique challenges.
ORB-SLAM3 offers robust visual SLAM capabilities.

Purpose of the Study:

To implement and evaluate ORB-SLAM3 for visual odometry on a low-power ARM-based system (Jetson Nano).
To assess the accuracy and limitations of ORB-SLAM3 for mobile robot tracking in diverse environments.

Main Methods:

Adaptation of the ORB-SLAM3 algorithm for ARM architecture.
System optimization, including software library selection and camera calibration.
Testing using the EuRoC dataset and real-world mobile robot experiments.

Main Results:

Accurate localization achieved, with path estimation errors between 3-11 cm on the EuRoC dataset.
Real-world tests identified discrepancies due to encoder drift and environmental factors (lighting, texture).

Conclusions:

ORB-SLAM3 is viable for low-power visual odometry on embedded systems.
Error mitigation strategies like enhanced calibration and sensor fusion are necessary for real-world applications.
Future work includes trajectory correction and improved integration with other robotic systems.