Error State Extended Kalman Filter Multi-Sensor Fusion for Unmanned Aerial Vehicle Localization in GPS and Magnetometer Denied Indoor Environments

TL;DR

This paper presents an error state extended Kalman filter for UAV indoor localization that fuses multiple sensor data sources to operate reliably without GPS or magnetometer measurements.

Contribution

It introduces an adapted ES-EKF framework for multi-sensor fusion in GPS/magnetometer denied environments, accounting for sensor drift and calibration errors.

Findings

Successful fusion of IMU, LiDAR, visual odometry, and UWB data.

Validation against ground truth demonstrates accurate localization.

Effective UAV position control in indoor environments.

Abstract

This paper addresses the issues of unmanned aerial vehicle (UAV) indoor navigation, specifically in areas where GPS and magnetometer sensor measurements are unavailable or unreliable. The proposed solution is to use an error state extended Kalman filter (ES -EKF) in the context of multi-sensor fusion. Its implementation is adapted to fuse measurements from multiple sensor sources and the state model is extended to account for sensor drift and possible calibration inaccuracies. Experimental validation is performed by fusing IMU data obtained from the PixHawk 2.1 flight controller with pose measurements from LiDAR Cartographer SLAM, visual odometry provided by the Intel T265 camera and position measurements from the Pozyx UWB indoor positioning system. The estimated odometry from ES-EKF is validated against ground truth data from the Optitrack motion capture system and its use in a position control loop to stabilize the UAV is demonstrated.