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Global localization of nano drone in an indoor environment

Olsson, Sofie LU (2020) In Master’s Theses in Mathematical Sciences FMAM05 20201
Mathematics (Faculty of Engineering)
Abstract
For a drone to be able to navigate in an indoor environment, it needs to understand its surroundings and locate itself to be able to plan a trajectory to its final destination. This thesis aims to solve the global localization problem, i.e. estimate a drone’s position and orientation in a previously mapped indoor environment by using a monocular camera and computer vision, which is an important first step towards autonomous navigation
To make a drone able to understand its surroundings, a camera is attached to it and computer vision algorithms are used to extract important information about features in the environment represented in images. Using an open source software, COLMAP the features can be recreated in a map. A feature in the map... (More)
For a drone to be able to navigate in an indoor environment, it needs to understand its surroundings and locate itself to be able to plan a trajectory to its final destination. This thesis aims to solve the global localization problem, i.e. estimate a drone’s position and orientation in a previously mapped indoor environment by using a monocular camera and computer vision, which is an important first step towards autonomous navigation
To make a drone able to understand its surroundings, a camera is attached to it and computer vision algorithms are used to extract important information about features in the environment represented in images. Using an open source software, COLMAP the features can be recreated in a map. A feature in the map is represented by a 3D-point and a descriptor, which describe the location and the structure of the feature in the world. Many points create together a point cloud. To be able to use the point cloud as a map for navigation, the scale ambiguity problem needs to be solved. Because of similarity properties of the projection model used in COLMAP, the point cloud can have arbitrary orientation and scale. A distance in the map can then be arbitrarily big, which makes it impossible to plan a trajectory. Therefore, the point cloud is rotated to match the orientation of the gravity direction and is scaled to metric scale by using sensor data from i.a. the drone’s IMU. By extracting features from images when the drone is flying, descriptors representing the features can be computed and compared with descriptors in the point cloud map. Point correspondences are then generated between the map and image. They are later used to solve the Perspective-three-point problem to derive a pose estimate of the drone in the environment. (Less)
Please use this url to cite or link to this publication:
author
Olsson, Sofie LU
supervisor
organization
course
FMAM05 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Global localization, Crazyflie, indoor navigation, SLAM, point cloud map, monocular camera, drone, computer vision
publication/series
Master’s Theses in Mathematical Sciences
report number
LUTFMA-3428-2020
ISSN
1404-6342
other publication id
2020:E70
language
English
id
9028733
date added to LUP
2020-09-10 14:50:12
date last changed
2020-09-10 14:50:12
@misc{9028733,
  abstract     = {For a drone to be able to navigate in an indoor environment, it needs to understand its surroundings and locate itself to be able to plan a trajectory to its final destination. This thesis aims to solve the global localization problem, i.e. estimate a drone’s position and orientation in a previously mapped indoor environment by using a monocular camera and computer vision, which is an important first step towards autonomous navigation
To make a drone able to understand its surroundings, a camera is attached to it and computer vision algorithms are used to extract important information about features in the environment represented in images. Using an open source software, COLMAP the features can be recreated in a map. A feature in the map is represented by a 3D-point and a descriptor, which describe the location and the structure of the feature in the world. Many points create together a point cloud. To be able to use the point cloud as a map for navigation, the scale ambiguity problem needs to be solved. Because of similarity properties of the projection model used in COLMAP, the point cloud can have arbitrary orientation and scale. A distance in the map can then be arbitrarily big, which makes it impossible to plan a trajectory. Therefore, the point cloud is rotated to match the orientation of the gravity direction and is scaled to metric scale by using sensor data from i.a. the drone’s IMU. By extracting features from images when the drone is flying, descriptors representing the features can be computed and compared with descriptors in the point cloud map. Point correspondences are then generated between the map and image. They are later used to solve the Perspective-three-point problem to derive a pose estimate of the drone in the environment.},
  author       = {Olsson, Sofie},
  issn         = {1404-6342},
  keyword      = {Global localization,Crazyflie,indoor navigation,SLAM,point cloud map,monocular camera,drone,computer vision},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master’s Theses in Mathematical Sciences},
  title        = {Global localization of nano drone in an indoor environment},
  year         = {2020},
}