Autonomous navigation and mapping for inspection of penstocks and tunnels with MAVs

Tolga Özaslan, Giuseppe Loianno, James Keller, Camillo J. Taylor, Vijay Kumar, Jennifer M. Wozencraft, Thomas Hood

Research output: Contribution to journalArticlepeer-review


In this paper, we address the estimation, control, navigation and mapping problems to achieve autonomous inspection of penstocks and tunnels using aerial vehicles with on-board sensing and computation. Penstocks and tunnels have the shape of a generalized cylinder. They are generally dark and featureless. State estimation is challenging because range sensors do not yield adequate information and cameras do not work in the dark. We show that the six degrees of freedom (DOF) pose and velocity can be estimated by fusing information from an inertial measurement unit (IMU), a lidar and a set of cameras. This letter discusses in detail the range-based estimation part while leaving the details of vision component to our earlier work. The proposed algorithm relies only on a model of the generalized cylinder and is robust to changes in shape of the tunnel. The approach is validated through real experiments showing autonomous and shared control, state estimation and environment mapping in the penstock at Center Hill Dam, TN. To our knowledge, this is the first time autonomous navigation and mapping has been achieved in a penstock without any external infrastructure such GPS or external cameras.

Original languageEnglish (US)
Article number7914761
Pages (from-to)1740-1747
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 2017


  • Aerial systems
  • field robots
  • perception and autonomy
  • robotics in hazardous fields

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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