Low-cost Autonomous Navigation System Based on Optical Flow Classification

TL;DR

This paper introduces a low-cost autonomous navigation system using a Raspberry Pi that employs optical flow pattern recognition and SVM classification to detect obstacles, achieving comparable performance to more expensive systems.

Contribution

The work demonstrates a cost-effective navigation approach combining optical flow analysis and machine learning on a Raspberry Pi, suitable for low-budget robotics.

Findings

System cost is lower than most existing solutions.

Performance is comparable to higher-cost systems.

Effective obstacle detection using optical flow and SVM.

Abstract

This work presents a low-cost robot, controlled by a Raspberry Pi, whose navigation system is based on vision. The strategy used consisted of identifying obstacles via optical flow pattern recognition. Its estimation was done using the Lucas-Kanade algorithm, which can be executed by the Raspberry Pi without harming its performance. Finally, an SVM-based classifier was used to identify patterns of this signal associated with obstacles movement. The developed system was evaluated considering its execution over an optical flow pattern dataset extracted from a real navigation environment. In the end, it was verified that the acquisition cost of the system was inferior to that presented by most of the cited works, while its performance was similar to theirs.