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This paper addresses the issues of unmanned aerial vehicle (UAV) indoornavigation, specifically in areas where GPS and magnetometer sensormeasurements are unavailable or unreliable. The proposed solution is to use anerror state extended Kalman filter (ES -EKF) in the context of multi-sensorfusion. Its implementation is adapted to fuse measurements from multiple sensorsources and the state model is extended to account for sensor drift andpossible calibration inaccuracies. Experimental validation is performed byfusing IMU data obtained from the PixHawk 2.1 flight controller with posemeasurements from LiDAR Cartographer SLAM, visual odometry provided by theIntel T265 camera and position measurements from the Pozyx UWB indoorpositioning system. The estimated odometry from ES-EKF is validated againstground truth data from the Optitrack motion capture system and its use in aposition control loop to stabilize the UAV is demonstrated.

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