Estimation, Mapping And Navigation With Micro Aerial Vehicles For Infrastructure Inspection

Loading...
Thumbnail Image
Degree type
Doctor of Philosophy (PhD)
Graduate group
Mechanical Engineering & Applied Mechanics
Discipline
Subject
Inspection
Mapping
Micro Aerial Vehicles
Quadcopter
SLAM
State estimation
Computer Sciences
Mechanical Engineering
Robotics
Funder
Grant number
License
Copyright date
2021-08-31T20:20:00-07:00
Distributor
Related resources
Author
Ozaslan, Tolga
Contributor
Abstract

Multi-rotor Micro Aerial Vehicles (MAV) have become popular robotic platforms in the last decade due to their manufacturability, agility and diverse payload options. Amongst the most promising applications areas of MAVs are inspection, air delivery, surveillance, search and rescue, real estate, entertainment and photography to name a few. While GPS offers an easy solution for outdoor autonomy, using onboard sensors is the only solution for autonomy in constrained indoor environments. In this work, we study onboard state estimation, mapping and navigation of a small MAV equipped with a minimal set of sensors inside GPS-denied, axisymmetric, tunnel-like environments such as penstocks. We primarily focus on state estimators formulated for different sensor suits which include 2D/3D lidars, cameras, and Inertial Measurement Units (IMU). Penstocks are pitch dark environments and offer very weak visual texture even with onboard illumination, hence our estimators primarily rely on lidars and IMU. The point cloud data returned by the lidar consists of either elliptical contours or indiscriminate partial cylindrical patches making localization along the tunnel axis theoretically impossible. Cameras track features on the walls using the onboard illumination to estimate the velocity along the tunnel axis unobservable to range sensors. Information from all sensors are then fused in a central Kalman Filter for 6 Degrees-of-Freedom (DOF) state estimation. These approaches are validated through onsite experiments conducted in four different dams demonstrating state estimation, environment mapping, autonomous and shared control.

Advisor
Vijay Kumar
Camilo J. Taylor
Date of degree
2020-01-01
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Recommended citation