Appearance-Based Navigation, Localization, Mapping, and Map Merging for Heterogeneous

167 Pages·2013·23.11 MB·English

UC Merced
UC Merced Electronic Theses and Dissertations
Title
Appearance-Based Navigation, Localization, Mapping, and Map Merging for Heterogeneous
Teams of Robots
Permalink
https://escholarship.org/uc/item/4hb1j7fs
Author
Erinc, Gorkem
Publication Date
2013

Peer reviewed|Thesis/dissertation
eScholarship.org Powered by the California Digital Library
University of California
University of California
Merced
Appearance-Based Navigation, Localization,
Mapping, and Map Merging for Heterogeneous
Teams of Robots
A dissertation submitted in partial satisfaction
of the requirements for the degree
Doctor of Philosophy
in
Electrical Engineering and Computer Science
by
Go¨rkem Erin¸c
2013
(cid:13)c Copyright by
Go¨rkem Erin¸c
2013
Abstract of the Dissertation
Appearance-Based Navigation, Localization,
Mapping, and Map Merging for Heterogeneous
Teams of Robots
by
Go¨rkem Erin¸c
Spatial awareness is a vital component for most autonomous robots operating
in unstructured environments. Appearance-based maps are emerging as an
important class of spatial representations for robots. Requiring only a cam-
era instead of an expensive sensor like a laser range finder, appearance-based
maps provide a suitable world model to human perception and offer a natural
way to exchange information between robots and humans. In this dissertation,
we embrace this representation and present a framework that provides navi-
gation, localization, mapping, and map merging capabilities to heterogeneous
multi-robot systems using exclusively monocular vision. Our first contribution
is integrating different ideas from separately proposed solutions into a robust
appearance-based localization and mapping framework that does not suffer
from the individual issues tied to the original proposed methods. Next, we
introduce a novel visual navigation algorithm that steers a robot between two
images through the shortest possible path. Thanks to its invariance to changes
in the tilt angle and the elevation of the cameras, the images collected by an-
other robot with a totally different morphology and camera placement can be
used for navigation. Furthermore, we tackle the problem of merging together
two or more appearance-based maps independently built by robots operat-
ing in the same environment, and propose an anytime algorithm aiming to
quickly identify the more advantageous parts to merge. Noting the lack of any
evaluation criteria for appearance-based maps, we introduce our task specific
quality metric that measures the utility of a map with respect to three major
robotic tasks: localization, mapping, and navigation. Additionally, in order to
measure the quality of merged appearance-based maps, and the performance
of the merging algorithm, we propose the use of algebraic connectivity, a con-
cept which we borrowed from graph theory. Finally, we introduce a machine
learning based WiFi localization technique which we later embrace as the core
of our novel heterogeneous map merging algorithm. All algorithms introduced
in this dissertation are validated on real robots.
ii
The dissertation of Go¨rkem Erin¸c is approved, and it is acceptable in quality and
form for publication on microfilm and electronically.
Ming-Hsuan Yang
David C. Noelle
Stefano Carpin, Committee Chair
University of California, Merced
2013
iii
Dedicated to my father...
iv
Table of Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Dissertation Contributions . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 Visual Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.1 PBVS methods . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1.2 IBVS methods . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Vision-based Localization and Mapping . . . . . . . . . . . . . . . . . 14
2.3 Map Merging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Wifi Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3 Visual Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.2 Camera Model . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1.3 Epipolar Geometry . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Navigation Algorithm for Heterogeneous Multi-Robot Systems . . . . 29
3.2.1 Single Robot Navigation . . . . . . . . . . . . . . . . . . . . . 29
3.2.2 Multi-Robot Navigation . . . . . . . . . . . . . . . . . . . . . 32
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.2 Implementation on a Multi-Robot System . . . . . . . . . . . 37
3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4 Appearance-Based Localization and Mapping . . . . . . . . . . . . . 43
4.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Image Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3 Data Structures and Training . . . . . . . . . . . . . . . . . . . . . . 47
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4.4 Localization in Appearance-Based Maps . . . . . . . . . . . . . . . . 48
4.5 Appearance-Based Map Building . . . . . . . . . . . . . . . . . . . . 51
4.6 Planning and Navigation on Appearance-Based Maps . . . . . . . . . 51
4.7 Quality Assessment of Appearance-Based Maps . . . . . . . . . . . . 55
4.7.1 Evaluation of Visual Maps . . . . . . . . . . . . . . . . . . . . 58
4.7.1.1 Localization . . . . . . . . . . . . . . . . . . . . . . . 60
4.7.1.2 Planning . . . . . . . . . . . . . . . . . . . . . . . . 64
4.7.1.3 Navigation . . . . . . . . . . . . . . . . . . . . . . . 69
4.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5 Appearance-Based Map Merging . . . . . . . . . . . . . . . . . . . . . 73
5.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2 Measuring the Quality of Merged Maps . . . . . . . . . . . . . . . . . 76
5.2.1 Defining Entanglement in Terms of Connectivity . . . . . . . . 76
5.2.2 Algebraic Connectivity . . . . . . . . . . . . . . . . . . . . . . 77
5.2.2.1 Properties of Algebraic Connectivity . . . . . . . . . 78
5.3 Merging Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.3.1 Merging Two Maps . . . . . . . . . . . . . . . . . . . . . . . . 81
5.3.2 Merging Multiple Maps . . . . . . . . . . . . . . . . . . . . . . 86
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6 Heterogeneous Map Merging . . . . . . . . . . . . . . . . . . . . . . . 94
6.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
6.3 WiFi Localization and Mapping . . . . . . . . . . . . . . . . . . . . . 99
6.3.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.3.1.1 Description . . . . . . . . . . . . . . . . . . . . . . . 101
6.3.1.2 Classification Algorithms . . . . . . . . . . . . . . . 101
6.3.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . 106
6.3.2 Regresion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
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6.3.2.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . 115
6.3.3 Monte Carlo Localization . . . . . . . . . . . . . . . . . . . . 115
6.3.3.1 Results . . . . . . . . . . . . . . . . . . . . . . . . . 117
6.4 Merging Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
6.4.1 Map Overlap Estimation . . . . . . . . . . . . . . . . . . . . . 119
6.4.1.1 OCC Algorithms . . . . . . . . . . . . . . . . . . . . 119
6.4.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.4.2 Probability Distribution Function . . . . . . . . . . . . . . . . 124
6.4.3 Edge-based Refinement and Regression . . . . . . . . . . . . . 125
6.4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
6.4.4.1 Full Map Merge . . . . . . . . . . . . . . . . . . . . . 127
6.4.4.2 Partial Map Merge . . . . . . . . . . . . . . . . . . . 127
6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
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List of Figures
3.1 Pinhole camera model . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Transformation from normalized coordinates to coordinates in pixels . 23
3.3 Epipolar geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4 Illustration of the visual navigation problem . . . . . . . . . . . . . . 30
3.5 Four-step navigation algorithm . . . . . . . . . . . . . . . . . . . . . 31
3.6 Tilt-correction process . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.7 Controller error profiles plotted for each stage of the navigation algo-
rithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.8 Distance and heading errors between the actual and target views plot-
ted during the four stages of the navigation algorithm . . . . . . . . . 37
3.9 Heterogeneous robot team used to test the navigation algorithm . . . 38
3.10 Sample results of the servoing algorithm tested on the Create robot . 39
3.11 Sample results of the servoing algorithm tested on the P3AT robot. . 40
3.12 Sample results of the servoing algorithm tested on the Create robot
using the map generated by the P3AT robot . . . . . . . . . . . . . . 41
3.13 Sample results of the servoing algorithm tested on the P3AT robot
using the map generated by the Create robot . . . . . . . . . . . . . . 42
4.1 Overview of dictionary learning, map building and localization proce-
dures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 A simple appearance-based map with edges inserted between suffi-
ciently similar images. . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.3 Sample image with extracted SIFT features . . . . . . . . . . . . . . 47
4.4 A representative set of random training images collected from online
repositories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.5 Performance comparison of majority voting schema and pairwise im-
age matching method . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.6 Snapshots of graphical user interface during map building process . . 52
4.7 Sample path followed to create an appearance-based map . . . . . . . 55
4.8 Waypoint images and their corresponding final views from both robots 56
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