Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM

This paper describes an image based visual servoing (IBVS) system for a nonholonomic robot to achieve good trajectory following without real-time robot pose information and without a known visual map of the environment. We call it trajectory servoing. The critical component is a featurebased, indirect SLAM method to provide a pool of available features with estimated depth, so that they may be propagated forward in time to generate image feature trajectories for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Trajectory servoing is shown to be more accurate than pose-based feedback when both rely on the same underlying SLAM system.

[1]  Nan Zhang,et al.  Learning Place-and-Time-Dependent Binary Descriptors for Long-Term Visual Localization , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Angela P. Schoellig,et al.  A Proof-of-Concept Demonstration of Visual Teach and Repeat on a Quadrocopter Using an Altitude Sensor and a Monocular Camera , 2014, 2014 Canadian Conference on Computer and Robot Vision.

[3]  François Chaumette,et al.  Visual servo control. I. Basic approaches , 2006, IEEE Robotics & Automation Magazine.

[4]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[5]  Sinisa Segvic,et al.  A mapping and localization framework for scalable appearance-based navigation , 2009, Comput. Vis. Image Underst..

[6]  Shigang Wang,et al.  Navigational Drift Analysis for Visual Odometry , 2015, Comput. Informatics.

[7]  Ivan Markovic,et al.  SOFT‐SLAM: Computationally efficient stereo visual simultaneous localization and mapping for autonomous unmanned aerial vehicles , 2018, J. Field Robotics.

[8]  Farbod Fahimi,et al.  An alternative closed-loop vision-based control approach for Unmanned Aircraft Systems with application to a quadrotor , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[9]  Philippe Martinet,et al.  Indoor Navigation of a Wheeled Mobile Robot along Visual Routes , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Patricio A. Vela,et al.  Real-Time Egocentric Navigation Using 3D Sensing , 2019, Machine Vision and Navigation.

[11]  Davide Scaramuzza,et al.  A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Yipu Zhao,et al.  Good Feature Matching: Toward Accurate, Robust VO/VSLAM With Low Latency , 2020, IEEE Transactions on Robotics.

[13]  Tom Duckett,et al.  Image features for visual teach-and-repeat navigation in changing environments , 2017, Robotics Auton. Syst..

[14]  Steve Furber,et al.  Navigating the Landscape for Real-Time Localization and Mapping for Robotics and Virtual and Augmented Reality , 2018, Proceedings of the IEEE.

[15]  Michael F. P. O'Boyle,et al.  SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[16]  François Chaumette,et al.  Path planning for robust image-based control , 2002, IEEE Trans. Robotics Autom..

[17]  Yoshiaki Shirai,et al.  Autonomous visual navigation of a mobile robot using a human-guided experience , 2002, Robotics Auton. Syst..

[18]  Stergios I. Roumeliotis,et al.  High-speed autonomous quadrotor navigation through visual and inertial paths , 2018, Int. J. Robotics Res..

[19]  Andrew Vardy.  Using feature scale change for robot localization along a route , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Patrick Gros,et al.  3D navigation based on a visual memory , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[21]  Michael F. P. O'Boyle,et al.  Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Jan Faigl,et al.  Navigation without localisation: reliable teach and repeat based on the convergence theorem , 2017, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[23]  Yipu Zhao,et al.  Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[24]  Cyrill Stachniss,et al.  Visual Servoing-based Navigation for Monitoring Row-Crop Fields , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[25]  Óscar Martínez Mozos,et al.  Predictive and adaptive maps for long-term visual navigation in changing environments , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[26]  Vijay Kumar,et al.  Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors , 2018, IEEE Robotics and Automation Letters.

[27]  Patricio A. Vela,et al.  PiPS: Planning in perception space , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Paul Timothy Furgale,et al.  Visual teach and repeat for long‐range rover autonomy , 2010, J. Field Robotics.

[29]  François Chaumette,et al.  Visual servo control. II. Advanced approaches [Tutorial] , 2007, IEEE Robotics & Automation Magazine.

[30]  Yi Lin,et al.  Autonomous aerial navigation using monocular visual‐inertial fusion , 2018, J. Field Robotics.

[31]  John J. Leonard,et al.  Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.

[32]  Ezio Malis,et al.  Preserving the continuity of visual servoing despite changing image features , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[33]  Giuseppe Oriolo,et al.  Visual servoing for path reaching with nonholonomic robots , 2011, Robotica.

[34]  Stephan Weiss,et al.  Vision based navigation for micro helicopters , 2012 .

[35]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

[36]  Sinisa Segvic,et al.  Experimental Evaluation of Autonomous Driving Based on Visual Memory and Image-Based Visual Servoing , 2011, IEEE Transactions on Intelligent Transportation Systems.