Survey on path and view planning for UAVs

Abstract Background In recent decades, unmanned aerial vehicles (UAVs) have developed rapidly and been widely applied in many domains, including photography, reconstruction, monitoring, and search and rescue. In such applications, one key issue is path and view planning, which tells UAVs exactly where to fly and how to search. Methods With specific consideration for three popular UAV applications (scene reconstruction, environment exploration, and aerial cinematography), we present a survey that should assist researchers in positioning and evaluating their works in the context of existing solutions. Results /Conclusions It should also help newcomers and practitioners in related fields quickly gain an overview of the vast literature. In addition to the current research status, we analyze and elaborate on advantages, disadvantages, and potential explorative trends for each application domain.

[1]  Wen-Hua Chen,et al.  Boustrophedon coverage path planning for UAV aerial surveys in wind , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[2]  Jiang Li,et al.  A Vision-Based Automatic Safe Landing-Site Detection System , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Simon Lacroix,et al.  Autonomous Detection of Safe Landing Areas for an UAV from Monocular Images , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Rahul Sukthankar,et al.  Cognitive Mapping and Planning for Visual Navigation , 2017, International Journal of Computer Vision.

[5]  Simon Fuhrmann,et al.  MVE - An image-based reconstruction environment , 2015, Comput. Graph..

[6]  Kam K. Leang,et al.  Autonomous Chemical-Sensing Aerial Robot for Urban/Suburban Environmental Monitoring , 2019, IEEE Systems Journal.

[7]  Rémi Ronfard,et al.  The Prose Storyboard Language: A Tool for Annotating and Directing Movies , 2015, FDG 2015.

[8]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

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

[10]  Rongjun Qin.  Analysis of critical parameters of satellite stereo image for 3D reconstruction and mapping , 2019, ArXiv.

[11]  Yahya H. Zweiri,et al.  Guided Next Best View for 3D Reconstruction of Large Complex Structures , 2019, Remote. Sens..

[12]  Rongjun Qin,et al.  RPC STEREO PROCESSOR (RSP) – A SOFTWARE PACKAGE FOR DIGITAL SURFACE MODEL AND ORTHOPHOTO GENERATION FROM SATELLITE STEREO IMAGERY , 2016 .

[13]  Christos Papachristos,et al.  Autonomous exploration and simultaneous object search using aerial robots , 2018, 2018 IEEE Aerospace Conference.

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

[15]  Alexander Domahidi,et al.  Real-time planning for automated multi-view drone cinematography , 2017, ACM Trans. Graph..

[16]  Jan-Michael Frahm,et al.  Structure-from-Motion Revisited , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Andreas Krause,et al.  Submodular Function Maximization , 2014, Tractability.

[18]  Dani Lischinski,et al.  Creating and chaining camera moves for quadrotor videography , 2018, ACM Trans. Graph..

[19]  Rolf Findeisen,et al.  Model predictive path-following for constrained nonlinear systems , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[20]  Luis Enrique Sucar,et al.  Supervised Learning of the Next-Best-View for 3D Object Reconstruction , 2019, Pattern Recognit. Lett..

[21]  Christos Papachristos,et al.  Uncertainty-aware receding horizon exploration and mapping using aerial robots , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Davide Scaramuzza,et al.  REMODE: Probabilistic, monocular dense reconstruction in real time , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[23]  J. Gong,et al.  3D Building Model Reconstruction from Multi-view Aerial Imagery and Lidar Data , 2011 .

[24]  Ali Farhadi,et al.  Target-driven visual navigation in indoor scenes using deep reinforcement learning , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[25]  Hao Yang,et al.  Uncut Aerial Video via a Single Sketch , 2018, Comput. Graph. Forum.

[26]  Roland Siegwart,et al.  Receding horizon path planning for 3D exploration and surface inspection , 2018, Auton. Robots.

[27]  Otmar Hilliges,et al.  Optimizing for aesthetically pleasing quadrotor camera motion , 2018, ACM Trans. Graph..

[28]  Roland Siegwart,et al.  An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments , 2020, IEEE Robotics and Automation Letters.

[29]  Roland Siegwart,et al.  Learning to Predict the Wind for Safe Aerial Vehicle Planning , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[30]  Pat Hanrahan,et al.  An interactive tool for designing quadrotor camera shots , 2015, ACM Trans. Graph..

[31]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[32]  Flavio Fontana,et al.  Continuous on-board monocular-vision-based elevation mapping applied to autonomous landing of micro aerial vehicles , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[33]  Geoffrey A. Hollinger,et al.  Multi-UAV exploration with limited communication and battery , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[34]  Pierre Feyzeau,et al.  Path planning: A 2013 survey , 2013, Proceedings of 2013 International Conference on Industrial Engineering and Systems Management (IESM).

[35]  Roland Siegwart,et al.  Safe Local Exploration for Replanning in Cluttered Unknown Environments for Microaerial Vehicles , 2017, IEEE Robotics and Automation Letters.

[36]  Marc Christie,et al.  Directing Cinematographic Drones , 2017, ACM Trans. Graph..

[37]  J. M. M. Montiel,et al.  ORB-SLAM: A Versatile and Accurate Monocular SLAM System , 2015, IEEE Transactions on Robotics.

[38]  Andrew E. Johnson,et al.  Lidar-Based Hazard Avoidance for Safe Landing on Mars , 2002 .

[39]  Pat Hanrahan,et al.  Submodular Trajectory Optimization for Aerial 3D Scanning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[40]  Silvio Savarese,et al.  A Behavioral Approach to Visual Navigation with Graph Localization Networks , 2019, Robotics: Science and Systems.

[41]  Otmar Hilliges,et al.  WYFIWYG: Investigating Effective User Support in Aerial Videography , 2018, ArXiv.

[42]  Pat Hanrahan,et al.  Towards a Drone Cinematographer: Guiding Quadrotor Cameras using Visual Composition Principles , 2016, ArXiv.

[43]  Danping Zou,et al.  Active Image-Based Modeling with a Toy Drone , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[44]  Pat Hanrahan,et al.  Generating dynamically feasible trajectories for quadrotor cameras , 2016, ACM Trans. Graph..

[45]  Yukinori Kobayashi,et al.  Autonomous Mapping and Exploration with Unmanned Aerial Vehicles Using Low Cost Sensors , 2018, Proceedings.

[46]  Roland Siegwart,et al.  Free LSD: Prior-Free Visual Landing Site Detection for Autonomous Planes , 2018, IEEE Robotics and Automation Letters.

[47]  Roland Siegwart,et al.  SFly: Swarm of micro flying robots , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[48]  Nathan Michael,et al.  Vision-based Landing Site Evaluation and Trajectory Generation Toward Rooftop Landing , 2014, Robotics: Science and Systems.

[49]  Vijay Kumar,et al.  Minimum snap trajectory generation and control for quadrotors , 2011, 2011 IEEE International Conference on Robotics and Automation.

[50]  Koren,et al.  Real-Time Obstacle Avoidance for Fast Mobile Robots , 2022 .

[51]  Daniel G. Aliaga,et al.  A Survey of Urban Reconstruction , 2013, Comput. Graph. Forum.

[52]  Filip Biljecki,et al.  Applications of 3D City Models: State of the Art Review , 2015, ISPRS Int. J. Geo Inf..

[53]  Emil Slusanschi,et al.  SafeUAV: Learning to Estimate Depth and Safe Landing Areas for UAVs from Synthetic Data , 2018, ECCV Workshops.

[54]  Titus Cieslewski,et al.  Rapid exploration with multi-rotors: A frontier selection method for high speed flight , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[55]  Roland Siegwart,et al.  Receding Horizon "Next-Best-View" Planner for 3D Exploration , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[56]  Daniel Cremers,et al.  Direct Sparse Odometry , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Dani Lischinski,et al.  Trip Synopsis: 60km in 60sec , 2016, Comput. Graph. Forum.

[58]  Charles Richter,et al.  Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments , 2016, ISRR.

[59]  Fredrik Heintz,et al.  Efficient Autonomous Exploration Planning of Large-Scale 3-D Environments , 2019, IEEE Robotics and Automation Letters.

[60]  Ramakant Nevatia,et al.  Symbolic Navigation with a Generic Map , 1999, Auton. Robots.

[61]  Quentin Galvane,et al.  Automated Cinematography with Unmanned Aerial Vehicles , 2016, WICED@Eurographics.

[62]  Xu Liu,et al.  Planetary landing site detection and selection using multilevel optimization strategy , 2019, Acta Astronautica.

[63]  Wen-Hua Chen,et al.  Optimal Polygon Decomposition for UAV Survey Coverage Path Planning in Wind , 2018, Sensors.

[64]  Ioannis Pitas,et al.  Challenges in Autonomous UAV Cinematography: An Overview , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).

[65]  Otmar Hilliges,et al.  Airways: Optimization-Based Planning of Quadrotor Trajectories according to High-Level User Goals , 2016, CHI.

[66]  Michael Goesele,et al.  Aerial path planning for urban scene reconstruction , 2018, ACM Trans. Graph..

[67]  Vladlen Koltun,et al.  Benchmarking Classic and Learned Navigation in Complex 3D Environments , 2019, ArXiv.

[68]  Anibal Ollero,et al.  Autonomous Cinematography with Teams of Drones , 2019 .

[69]  Sebastian Scherer,et al.  Towards a Robust Aerial Cinematography Platform: Localizing and Tracking Moving Targets in Unstructured Environments , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[70]  Sebastian Scherer,et al.  3D Convolutional Neural Networks for landing zone detection from LiDAR , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).