Abstract
This paper addresses the problem of autonomous navigation of a micro aerial vehicle (MAV) inside a constrained shipboard environment for inspection and damage assessment, which might be perilous or inaccessible for humans especially in emergency scenarios. The environment is GPS-denied and visually degraded, containing narrow passageways, doorways and small objects protruding from the wall. This makes existing 2D LIDAR, vision or mechanical bumper-based autonomous navigation solutions fail. To realize autonomous navigation in such challenging environments, we propose a fast and robust state estimation algorithm that fuses estimates from a direct depth odometry method and a Monte Carlo localization algorithm with other sensor information in an EKF framework. Then, an online motion planning algorithm that combines trajectory optimization with receding horizon control framework is proposed for fast obstacle avoidance. All the computations are done in real-time onboard our customized MAV platform. We validate the system by running experiments in different environmental conditions. The results of over 10 runs show that our vehicle robustly navigates 20 m long corridors only 1 m wide and goes through a very narrow doorway (66 cm width, only 4 cm clearance on each side) completely autonomously even when it is completely dark or full of light smoke.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Grzonka, S., Grisetti, G., Burgard, W.: A fully autonomous indoor quadrotor. IEEE Trans. Robot. 28(1), 90–100 (2012)
Dryanovski, I., Valenti, R.G., Xiao, J.: An open-source navigation system for micro aerial vehicles. Auton. Robots 34(3), 177–188 (2013)
Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained MAV. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 20–25. IEEE (2011)
Schauwecker, K., Zell, A.: On-board dual-stereo-vision for the navigation of an autonomous MAV. J. Intell. Robot. Syst. Theory Appl. 74, 1–16 (2014)
Fraundorfer, F., Heng, L., Honegger, D.: Vision-based autonomous mapping and exploration using a quadrotor MAV. In: IEEE International Conference on Intelligent Robots and Systems, pp. 4557–4564 (2012)
Wu, A.D., Johnson, E.N., Kaess, M., et al.: Autonomous flight in GPS-denied environments using monocular vision and inertial sensors. J. Aerosp. Inf. Syst. 10, 172–186 (2013)
Scaramuzza, D., Achtelik, M., Doitsidis, L., et al.: Vision-controlled micro flying robots: from system design to autonomous navigation and mapping in GPS-denied environments, pp. 26–40 (2014)
Weiss, S., Scaramuzza, D., Siegwart, R.: Monocular-slam-based navigation for autonomous micro helicopters in GPS-denied environments. J. Field Robot. 28(6), 854–874 (2011)
Flores, G., Zhou, S., Lozano, R., Castillo, P.: A vision and GPS-based real-time trajectory planning for a MAV in unknown and low-sunlight environments. J. Intell. Robot. Syst. 74(1–2), 59–67 (2014)
Huang, A.S., Bachrach, A.: Visual odometry and mapping for autonomous flight using an RGB-D camera. Int. Symp. Robot. Res. 1–16 (2011)
Valenti, R.G., Dryanovski, I., Jaramillo, C.: Autonomous quadrotor flight using onboard RGB-D visual odometry. In: 2014 IEEE International Conference on Robotics and Automation, pp. 5233–5238. IEEE (2014)
Fang, Z., Scherer, S.: Real-time onboard 6DoF localization of an indoor MAV in degraded visual environments using a RGB-D camera. In: 2015 IEEE International Conference on Robotics and Automation, May 2015
Horn, B.K.P., Harris, J.G.: Rigid body motion from range image sequences. CVGIP Image Underst. 53(1), 1–13 (1991)
Pomerleau, F., Colas, F., Siegwart, R., Magnenat, S.: Comparing ICP variants on real-world data sets. Auton. Robots 34(3), 133–148 (2013)
Callaghan, K., Chen, J.: Revisiting the collinear data problem: an assessment of estimator Ill-conditioning in linear regression. Pract. Assess. Res. Eval. 13(5), 5 (2008)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press (2005)
Scherer, S., Rehder, J., Achar, S., et al.: River mapping from a flying robot: state estimation, river detection, and obstacle mapping. Auton. Robots 33(1–2), 189–214 (2012)
Green, C.J., Kelly, A.: Optimal sampling in the space of paths: Preliminary results (2006)
Ratliff, N., Zucker, M., Bagnell, J.A., et al.: Chomp: gradient optimization techniques for efficient motion planning. In: 2009 IEEE International Conference on Robotics and Automation, pp. 489–494 (2009)
Mellinger, D., Kumar, V.: Minimum snap trajectory generation and control for quadrotors. In: 2011 IEEE International Conference on Robotics and Automation, pp. 2520–2525 (2011)
Richter, C., Bry, A., Roy, N.: Polynomial trajectory planning for quadrotor flight. In: International Conference on Robotics and Automation (2013)
Golub, G.H., Hansen, P.C., O’Leary, D.P.: Tikhonov regularization and total least squares. SIAM J. Matrix Anal. Appl. 21(1), 185–194 (1999)
Zhang, J., Singh, S.: LOAM : Lidar Odometry and Mapping in Real-time. In: Robotics: Science and Systems Conference (RSS) (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Fang, Z. et al. (2016). Robust Autonomous Flight in Constrained and Visually Degraded Environments. In: Wettergreen, D., Barfoot, T. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-27702-8_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-27702-8_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27700-4
Online ISBN: 978-3-319-27702-8
eBook Packages: EngineeringEngineering (R0)