Date of Award

2020

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Mechanical Engineering & Applied Mechanics

First Advisor

Vijay Kumar

Second Advisor

Camilo J. Taylor

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.

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