Advanced Autonomy on a Low-Cost Educational Drone Platform

TL;DR

This paper introduces PiDrone, an affordable educational quadrotor with onboard autonomous capabilities including state estimation, localization, and SLAM, implemented in Python for use in teaching robotics.

Contribution

It presents a complete hardware and software framework for autonomous flight on a low-cost drone, enabling high-level planning and mapping in educational environments.

Findings

UKF provides accurate state estimation compared to ground truth.

Localization and SLAM algorithms run efficiently on Raspberry Pi.

Framework achieves real-time autonomous operation suitable for teaching.

Abstract

PiDrone is a quadrotor platform created to accompany an introductory robotics course. Students build an autonomous flying robot from scratch and learn to program it through assignments and projects. Existing educational robots do not have significant autonomous capabilities, such as high-level planning and mapping. We present a hardware and software framework for an autonomous aerial robot, in which all software for autonomy can run onboard the drone, implemented in Python. We present an Unscented Kalman Filter (UKF) for accurate state estimation. Next, we present an implementation of Monte Carlo (MC) Localization and FastSLAM for Simultaneous Localization and Mapping (SLAM). The performance of UKF, localization, and SLAM is tested and compared to ground truth, provided by a motion-capture system. Our evaluation demonstrates that our autonomous educational framework runs quickly and accurately on a Raspberry Pi in Python, making it ideal for use in educational settings.