LoRa Backscatter Assisted State Estimator for Micro Aerial Vehicles with Online Initialization

TL;DR

Marvel introduces an RF backscatter-based system enabling autonomous MAV navigation in dim environments with online calibration, achieving 34 cm localization accuracy over 50 meters without internal hardware modifications.

Contribution

The paper presents a novel backscatter-based state estimation system with online initialization for MAVs, functioning without internal hardware changes and in low-light conditions.

Findings

Supports navigation within 50 meters and through three walls

Achieves 34 cm localization accuracy and 4.99° orientation accuracy

Demonstrates effective online initialization and calibration

Abstract

The advances in agile micro aerial vehicles (MAVs) have shown great potential in replacing humans for labor-intensive or dangerous indoor investigation, such as warehouse management and fire rescue. However, the design of a state estimation system that enables autonomous flight poses fundamental challenges in such dim or smoky environments. Current dominated computer-vision based solutions only work in well-lighted texture-rich environments. This paper addresses the challenge by proposing Marvel, an RF backscatter-based state estimation system with online initialization and calibration. Marvel is nonintrusive to commercial MAVs by attaching backscatter tags to their landing gears without internal hardware modifications, and works in a plug-and-play fashion with an automatic initialization module. Marvel is enabled by three new designs, a backscatter-based pose sensing module, an online initialization and calibration module, and a backscatter-inertial super-accuracy state estimation algorithm. We demonstrate our design by programming a commercial MAV to autonomously fly in different trajectories. The results show that Marvel supports navigation within a range of 50 m or through three concrete walls, with an accuracy of 34 cm for localization and 4.99 degrees for orientation estimation. We further demonstrate our online initialization and calibration by comparing to the perfect initial parameter measurements from burdensome manual operations.