Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.

[1]  Abraham Bachrach,et al.  Autonomous flight in unstructured and unknown indoor environments , 2009 .

[2]  Sven Behnke,et al.  TOWARDS MULTIMODAL OMNIDIRECTIONAL OBSTACLE DETECTION FOR AUTONOMOUS UNMANNED AERIAL VEHICLES , 2013 .

[3]  Sven Behnke,et al.  Obstacle detection and navigation planning for autonomous micro aerial vehicles , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[4]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[5]  Stefan Kohlbrecher,et al.  A flexible and scalable SLAM system with full 3D motion estimation , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[6]  Paul Newman,et al.  Lost in translation (and rotation): Rapid extrinsic calibration for 2D and 3D LIDARs , 2012, 2012 IEEE International Conference on Robotics and Automation.

[7]  Alessandro De Gloria,et al.  Multi-robot search and rescue team , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[8]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[9]  Wolfgang Förstner,et al.  INCREMENTAL REAL-TIME BUNDLE ADJUSTMENT FOR MULTI-CAMERA SYSTEMS WITH POINTS AT INFINITY , 2013 .

[10]  Andreas Zell,et al.  On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV , 2013, Journal of Intelligent & Robotic Systems.

[11]  Eric N. Johnson,et al.  Monocular Visual Mapping for Obstacle Avoidance on UAVs , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[12]  Edwin Olson,et al.  AprilTag: A robust and flexible visual fiducial system , 2011, 2011 IEEE International Conference on Robotics and Automation.

[13]  Shuzhi Sam Ge,et al.  Dynamic Motion Planning for Mobile Robots Using Potential Field Method , 2002, Auton. Robots.

[14]  Wolfram Burgard,et al.  A Fully Autonomous Indoor Quadrotor , 2012, IEEE Transactions on Robotics.

[15]  Greg,et al.  Flight Control Law Design and Development For An Autonomous Rotorcraft , 2008 .

[16]  Andreas Nüchter,et al.  6DOF semi-rigid SLAM for mobile scanning , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Sebastian Thrun,et al.  Junior: The Stanford entry in the Urban Challenge , 2008, J. Field Robotics.

[18]  Jörg Stückler,et al.  Multi-resolution surfel maps for efficient dense 3D modeling and tracking , 2014, J. Vis. Commun. Image Represent..

[19]  Jur P. van den Berg,et al.  Automatic collision avoidance for manually tele-operated unmanned aerial vehicles , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Marc Pollefeys,et al.  Autonomous Visual Mapping and Exploration With a Micro Aerial Vehicle , 2014, J. Field Robotics.

[21]  Jörg Stückler,et al.  Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles , 2016, J. Field Robotics.

[22]  Michael Bosse,et al.  Continuous 3D scan-matching with a spinning 2D laser , 2009, 2009 IEEE International Conference on Robotics and Automation.

[23]  Martial Hebert,et al.  Learning monocular reactive UAV control in cluttered natural environments , 2012, 2013 IEEE International Conference on Robotics and Automation.

[24]  Youdan Kim,et al.  3D Shape Mapping of Obstacle Using Stereo Vision Sensor on Quadrotor UAV , 2014 .

[25]  Radhakant Padhi,et al.  Reactive Collision Avoidance of UAVs with Stereovision Camera Sensors using UKF , 2014 .

[26]  Albert S. Huang,et al.  Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments , 2012, Int. J. Robotics Res..

[27]  Sven Behnke,et al.  Hierarchical Planning with 3D Local Multiresolution Obstacle Avoidance for Micro Aerial Vehicles , 2014, ISR 2014.

[28]  Andrew Nolan,et al.  Obstacle mapping module for quadrotors on outdoor Search and Rescue operations , 2013 .

[29]  Sven Behnke,et al.  Nonlinear Model-based Position Control for Quadrotor UAVs , 2014, ISR 2014.

[30]  A. Nuchter,et al.  6D SLAM with approximate data association , 2005, ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005..

[31]  Sebastian Scherer,et al.  Sparse Tangential Network (SPARTAN): Motion planning for micro aerial vehicles , 2013, 2013 IEEE International Conference on Robotics and Automation.

[32]  Jörg Stückler,et al.  Multi-resolution surfel mapping and real-time pose tracking using a continuously rotating 2D laser scanner , 2013, 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).

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

[34]  Sebastian Thrun,et al.  Scan Alignment and 3-D Surface Modeling with a Helicopter Platform , 2003, FSR.

[35]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D-NDT: Research Articles , 2007 .

[36]  Nicholas Roy,et al.  Autonomous Flight in Unknown Indoor Environments , 2009 .

[37]  Kazuya Yoshida,et al.  Collaborative mapping of an earthquake‐damaged building via ground and aerial robots , 2012, J. Field Robotics.

[38]  Wolfram Burgard,et al.  Coastal navigation-mobile robot navigation with uncertainty in dynamic environments , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[39]  Tim D. Barfoot,et al.  Towards relative continuous-time SLAM , 2013, 2013 IEEE International Conference on Robotics and Automation.

[40]  Karl Tuyls,et al.  OctoSLAM: A 3D mapping approach to situational awareness of unmanned aerial vehicles , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[41]  Achim J. Lilienthal,et al.  Maximum likelihood point cloud acquisition from a mobile platform , 2009, 2009 International Conference on Advanced Robotics.

[42]  Teodor Tomic,et al.  Autonomous Vision‐based Micro Air Vehicle for Indoor and Outdoor Navigation , 2014, J. Field Robotics.

[43]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[44]  Rogelio Lozano,et al.  A Vision and GPS-Based Real-Time Trajectory Planning for a MAV in Unknown and Low-Sunlight Environments , 2014, J. Intell. Robotic Syst..

[45]  Wolfram Burgard,et al.  G2o: A general framework for graph optimization , 2011, 2011 IEEE International Conference on Robotics and Automation.

[46]  Tom Duckett,et al.  Scan registration for autonomous mining vehicles using 3D‐NDT , 2007, J. Field Robotics.

[47]  Sven Behnke,et al.  Layered Mission and Path Planning for MAV Navigation with Partial Environment Knowledge , 2014, IAS.

[48]  Marc Pollefeys,et al.  PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision , 2012, Auton. Robots.

[49]  Roland Siegwart,et al.  Motion‐ and Uncertainty‐aware Path Planning for Micro Aerial Vehicles , 2014, J. Field Robotics.

[50]  Huosheng Hu,et al.  3D mapping with multi-resolution occupied voxel lists , 2010, Auton. Robots.

[51]  Darius Burschka,et al.  Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue , 2012, IEEE Robotics & Automation Magazine.

[52]  Wolfram Burgard,et al.  Towards a navigation system for autonomous indoor flying , 2009, 2009 IEEE International Conference on Robotics and Automation.

[53]  Sven Behnke,et al.  A high-performance MAV for autonomous navigation in complex 3D environments , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[54]  Sebastian Scherer,et al.  First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles , 2013, 2013 IEEE International Conference on Robotics and Automation.

[55]  Vijay Kumar,et al.  Multi-sensor fusion for robust autonomous flight in indoor and outdoor environments with a rotorcraft MAV , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[56]  Jonghyuk Kim,et al.  Integrated navigation system using camera and gimbaled laser scanner for indoor and outdoor autonomous flight of UAVs , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[57]  Jörg Stückler,et al.  Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[58]  Jizhong Xiao,et al.  3 D Indoor Mapping for Micro-UAVs Using Hybrid Range Finders and Multi-Volume Occupancy Grids , 2010 .

[59]  Jörg Stückler,et al.  Local Multi-resolution Surfel Grids for MAV Motion Estimation and 3D Mapping , 2014, IAS.

[60]  Maxim Likhachev,et al.  Path planning for non-circular micro aerial vehicles in constrained environments , 2013, 2013 IEEE International Conference on Robotics and Automation.