Accurate 3D Localization for MAV Swarms by UWB and IMU Fusion
This provides a more reliable localization solution for MAV swarms in applications like indoor light shows, but it is incremental as it builds on existing sensor fusion methods.
The paper tackled the problem of high latency and low bandwidth in 3D localization for Micro Aerial Vehicle (MAV) swarms by proposing an Extended Kalman Filter-based algorithm that fuses Ultra Wideband (UWB) and Inertial Measurement Unit (IMU) data, achieving 80Hz localization with significantly improved accuracy and almost no delay.
Driven by applications like Micro Aerial Vehicles (MAVs), driver-less cars, etc, localization solution has become an active research topic in the past decade. In recent years, Ultra Wideband (UWB) emerged as a promising technology because of its impressive performance in both indoor and outdoor positioning. But algorithms relying only on UWB sensor usually result in high latency and low bandwidth, which is undesirable in some situations such as controlling a MAV. To alleviate this problem, an Extended Kalman Filter (EKF) based algorithm is proposed to fuse the Inertial Measurement Unit (IMU) and UWB, which achieved 80Hz 3D localization with significantly improved accuracy and almost no delay. To verify the effectiveness and reliability of the proposed approach, a swarm of 6 MAVs is set up to perform a light show in an indoor exhibition hall. Video and source codes are available at https://github.com/lijx10/uwb-localization