Autonomous Navigation of Micro Air Vehicles in Warehouses Using Vision-based Line Following

TL;DR

This paper presents a vision-based navigation system for micro air vehicles in warehouses, utilizing a single camera, color detection, line detection, and Kalman filtering, validated through Gazebo simulations to improve autonomous indoor logistics.

Contribution

It introduces a novel MAV navigation approach using a single camera and vision algorithms tailored for warehouse environments, validated through simulation.

Findings

Successful navigation in narrow indoor spaces

Effective line detection with HSV and Hough Transform

Reliable line tracking with Kalman filter

Abstract

In this paper, we propose a vision-based solution for indoor Micro Air Vehicle (MAV) navigation, with a primary focus on its application within autonomous warehouses. Our work centers on the utilization of a single camera as the primary sensor for tasks such as detection, localization, and path planning. To achieve these objectives, we implement the HSV color detection and the Hough Line Transform for effective line detection within warehouse environments. The integration of a Kalman filter into our system enables the camera to track yellow lines reliably. We evaluated the performance of our vision-based line following algorithm through various MAV flight tests conducted in the Gazebo 11 platform, utilizing ROS Noetic. The results of these simulations demonstrate the system capability to successfully navigate narrow indoor spaces. Our proposed system has the potential to significantly reduce labor costs and enhance overall productivity in warehouse operations. This work contributes to the growing field of MAV applications in autonomous warehouses, addressing the need for efficient logistics and supply chain solutions.