Towards fully autonomous visual inspection of dark featureless dam penstocks using MAVs

Özaslan T. , Mohta K., Keller J., Mulgaonkar Y., Taylor C. J. , Kumar V., ...More

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, South Korea, 9 - 14 October 2016, pp.4998-5005 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/iros.2016.7759734
  • City: Daejeon
  • Country: South Korea
  • Page Numbers: pp.4998-5005


© 2016 IEEE.In the last decade, multi-rotor Micro Aerial Vehicles (MAVs) have attracted great attention from robotics researchers. Offering affordable agility and maneuverability, multi-rotor aircrafts have become the most commonly used platforms for robotics applications. Amongst the most promising applications are inspection of power-lines, cell-towers, large and constrained infrastructures and precision agriculture. While GPS offers an easy solution for outdoor autonomy, using onboard sensors is the only solution for autonomy in constrained indoor environments. In this paper, we present our results on autonomous inspection of completely dark, featureless, symmetric dam penstocks using cameras and range sensors. We use a hex-rotor platform equipped with an IMU, four cameras and two lidars. One of the cameras tracks features on the walls using the on-board illumination to estimate the position along the tunnel axis unobservable to range sensors while all of the cameras are used for panoramic image construction. The two lidars estimate the remaining degrees of freedom (DOF). Outputs of the two estimators are fused using an Unscented Kalman Filter (UKF). A moderately trained operator defines waypoints using the Remote Control (RC). We demonstrate our results from Carters Dam, GA and Glen Canyon Dam, AZ which include panoramic images for cracks and rusty spot detection and 6-DOF estimation results with ground truth comparisons. To our knowledge ours is the only study that can autonomously inspect environments with no geometric cues and poor to no external illumination using MAVs.