An Integrated MAV-RFID System for Geo-referenced Monitoring of Harsh Environments

Micro Aerial Vehicles (MAVs) equipped with lightweight Radio Frequency Identification (RFID) sensor dataloggers, have the potential to assist in achieving environmental awareness in a large range of situations. However, in order to gain such insight, the system must be able to accurately localize itself and fuse any readings of its surroundings into a consistent map. In this paper we demonstrate how camera, IMU and environmental data obtained with an RFID-enabled temperature sensor may be merged together to create accurate 3D maps along the MAV curvilinear trajectories in unknown locations. The idea is demonstrated through experimentations in both indoor and outdoor harsh environments.

[1]  Roland Siegwart,et al.  Robust visual inertial odometry using a direct EKF-based approach , 2015, IROS 2015.

[2]  Shaojie Shen,et al.  VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator , 2017, IEEE Transactions on Robotics.

[3]  Marc Pollefeys,et al.  PIXHAWK: A system for autonomous flight using onboard computer vision , 2011, 2011 IEEE International Conference on Robotics and Automation.

[4]  Michael Bosse,et al.  Keyframe-based visual–inertial odometry using nonlinear optimization , 2015, Int. J. Robotics Res..

[5]  Stergios I. Roumeliotis,et al.  A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[6]  Nicholas Roy,et al.  RANGE–Robust autonomous navigation in GPS‐denied environments , 2011, J. Field Robotics.

[7]  G. Marrocco,et al.  The Interrogation Footprint of RFID-UAV: Electromagnetic Modeling and Experimentations , 2017, IEEE Journal of Radio Frequency Identification.

[8]  Gaetano Marrocco,et al.  Flying sensors: Merging Nano-UAV with radiofrequency identification , 2017, 2017 IEEE International Conference on RFID Technology & Application (RFID-TA).

[9]  Frank Dellaert,et al.  Information fusion in navigation systems via factor graph based incremental smoothing , 2013, Robotics Auton. Syst..

[10]  Roland Siegwart,et al.  Maplab: An Open Framework for Research in Visual-Inertial Mapping and Localization , 2017, IEEE Robotics and Automation Letters.

[11]  G. Marrocco,et al.  RFIDrone: Preliminary experiments and electromagnetic models , 2016, 2016 URSI International Symposium on Electromagnetic Theory (EMTS).

[12]  Roland Siegwart,et al.  Voxblox: Incremental 3D Euclidean Signed Distance Fields for on-board MAV planning , 2016, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[13]  Roland Siegwart,et al.  Vision Based Position Control for MAVs Using One Single Circular Landmark , 2011, J. Intell. Robotic Syst..

[14]  Jörg Stückler,et al.  Direct visual-inertial odometry with stereo cameras , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[15]  Gaetano Marrocco,et al.  Ubiquitous Flying Sensor Antennas: Radiofrequency Identification Meets Micro Drones , 2017, IEEE Journal of Radio Frequency Identification.

[16]  Hirobumi Saito,et al.  In-orbit Performance Evaluation of Temperature Controlled Small Fiber Optical Gyro on Microsattelite ”REIMEI” , 2006 .

[17]  Vijay Kumar,et al.  Autonomous multi-floor indoor navigation with a computationally constrained MAV , 2011, 2011 IEEE International Conference on Robotics and Automation.