CiUAV: A Multi-Task 3D Indoor Localization System for UAVs based on Channel State Information

TL;DR

CiUAV is a cost-effective 3D indoor UAV localization system using CSI from low-cost sensors, employing AGC compensation and a multi-task model to achieve high accuracy and robustness in GPS-denied environments.

Contribution

The paper introduces a novel multi-task 3D localization model and an AGC compensation algorithm, enhancing robustness and accuracy of indoor UAV positioning with low-cost sensors.

Findings

Achieves 0.2629 m LMSE localization error in 3D space.

Demonstrates robustness in resource-constrained indoor environments.

Provides a scalable, cost-effective UAV indoor localization solution.

Abstract

Accurate indoor positioning for unmanned aerial vehicles (UAVs) is critical for logistics, surveillance, and emergency response applications, particularly in GPS-denied environments. Existing indoor localization methods, including optical tracking, ultra-wideband, and Bluetooth-based systems, face cost, accuracy, and robustness trade-offs, limiting their practicality for UAV navigation. This paper proposes CiUAV, a novel 3D indoor localization system designed for UAVs, leveraging channel state information (CSI) obtained from low-cost ESP32 IoT-based sensors. The system incorporates a dynamic automatic gain control (AGC) compensation algorithm to mitigate noise and stabilize CSI signals, significantly enhancing the robustness of the measurement. Additionally, a multi-task 3D localization model, Sensor-in-Sample (SiS), is introduced to enhance system robustness by addressing challenges related to incomplete sensor data and limited training samples. SiS achieves this by joint training with varying sensor configurations and sample sizes, ensuring reliable performance even in resource-constrained scenarios. Experiment results demonstrate that CiUAV achieves a LMSE localization error of 0.2629 m in a 3D space, achieving good accuracy and robustness. The proposed system provides a cost-effective and scalable solution, demonstrating its usefulness for UAV applications in resource-constrained indoor environments.