-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathpage1.html
More file actions
299 lines (258 loc) · 15.1 KB
/
page1.html
File metadata and controls
299 lines (258 loc) · 15.1 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>IPCV Research Group</title>
<link rel="stylesheet" href="css/normalize.css">
<link rel="stylesheet" href="css/style.css">
<link rel="icon" href="images/favicon_2.png">
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Poppins&family=Roboto+Condensed&display=swap" rel="stylesheet">
</head>
<body>
<header>
<nav>
<ul class="navBar">
<li class="navBar"><a href="index.html">Home</a></li>
<li class="navBar"><a href="#Abstract">Abstract</a></li>
<li class="navBar"><a href="#Datasets">Datasets</a></li>
<li class="navBar"><a href="#Environments">Environments</a></li>
<li class="navBar"><a href="#Results">Results</a></li>
<li class="navBar"><a href="#Future_Work">Future Work</a></li>
<li class="navBar"><a href="#Publication">Publication</a></li>
</ul>
</nav>
</header>
<h1>Comparing and Evaluating Indoor Positioning Techniques</h1>
<a id="Abstract">
<h2>Abstract</h2>
<div class="container-1400">
<div class="paragraph">
<p> Accurate indoor positioning has been the subject of investigation for many years. Modern smartphones have a wide suite of internal sensors that allow us to measure different signals. However, traditional positioning methods, such as GPS, typically fail when measurements are taken indoors. Many different solutions have been proposed that rely on various data inputs, including Wi-Fi and camera input. Some proposed methods have used auxiliary data inputs such as BLE Beacons. However, any auxiliary input would require additional infrastructure to purchase and maintain, increasing expenses. This paper explores a variety of indoor positioning techniques that do not require any additional infrastructure beyond what is typically found in a commercial environment. This research explores, implements, and measures, through standardized tests, Wi-Fi RSSI, RTT, and marker-based trilateration, as well as, fingerprinting with two separate machine learning models, and also tests an implementation of PoseNet. It compares and contrasts the various methods, categorizing them according to a proposed set of criteria for evaluating a commercially deployable indoor positioning solution. The paper closes with a brief summary of the techniques that were studied and proposes investigation into various related topics and improvements, as well as future directions.</p>
</div>
</div>
<h2>Techniques Explored</h2>
<div class="container-1400">
<div class="paragraph">
<p> The techniques that we explored were RSSI Trilateration, RTT Trilateration, Marker-based Trilateration, RSSI Fingerprinting, RTT Fingerprinting, Marker-based Fingerprinting, and PoseNet. The fingerprinting techniques included both k-nearest neighbors and neural network implementations.</p>
</div>
</div>
</a>
<a id="Datasets">
<h2>Datasets</h2>
<div class="container-1400">
<div class="links">
<a href="https://github.com/indoorpositioning/indoorpositioning/tree/main/Datasets/Hacklab">Hacklab</a>
<a href="https://github.com/indoorpositioning/indoorpositioning/tree/main/Datasets/TARoom/Marker-based">TA Room - Marker-based Data</a>
<a href="https://github.com/indoorpositioning/indoorpositioning/tree/main/Datasets/TARoom/RTT">TA Room - RTT Data</a>
<a href="https://github.com/indoorpositioning/indoorpositioning/tree/main/Datasets/TARoom/RSSI">TA Room - RSSI Data</a>
</div>
</div>
</a>
<a id="Environments">
<h2>Environments</h2>
<h3>HackLab</h3>
<div class="images">
<img src="./images/HLAB_152.jpg" alt="Image of HackLab" style="width: 400px;">
<img src="./images/HLAB_835.jpg" alt="Image of HackLab" style="width: 400px;">
<img src="./images/HLAB_841.jpg" alt="Image of HackLab" style="width: 400px;">
<img src="./images/HLAB_952.jpg" alt="Image of HackLab" style="width: 400px;">
<img src="./images/HLAB_962.jpg" alt="Image of HackLab" style="width: 400px;">
</div>
<!-- <h5>3D Scan of HackLab</h5> -->
<h3>TA Room</h3>
<div class="images">
<img src="./images/TA (1).jpg" alt="Image of TA Room" style="width: 400px;">
<img src="./images/TA (2).jpg" alt="Image of TA Room" style="width: 400px;">
<img src="./images/TA (3).jpg" alt="Image of TA Room" style="width: 400px;">
<img src="./images/TA (10).jpg" alt="Image of TA Room" style="width: 400px;">
<img src="./images/TA (11).jpg" alt="Image of TA Room" style="width: 400px;">
<img src="./images/TA (12).jpg" alt="Image of TA Room" style="width: 400px;">
</div>
<h4>Setup Example</h4>
<div class="images">
<img src="./images/TA (7).jpg" alt="Image of TA Room" style="width: 400px;">
<img src="./images/TA (8).jpg" alt="Image of TA Room" style="width: 400px;">
</div>
</a>
<br>
<a id="Results">
<h2>Results</h2>
<h5>Note: The grid squares are 1m x 1m</h5>
<h4>Marker-based Positioning Results</h4>
<div class="images">
<figure class="left">
<img src="./images/ar_gt.png" alt="">
<figcaption>Ground Truth</figcaption>
</figure>
<figure class="middle">
<img src="./images/ar_result.png" alt="">
<figcaption>Neural Net Results</figcaption>
</figure>
<figure class="right">
<img src="./images/ar_result_knn.png" alt="">
<figcaption>kNN Results</figcaption>
</figure>
</div>
<h4>RSSI Results</h4>
<div class="images">
<figure class="left">
<img src="./images/rssi_gt.png" alt="">
<figcaption>Ground Truth</figcaption>
</figure>
<figure class="middle">
<img src="./images/rssi_result.png" alt="">
<figcaption>Neural Net Results</figcaption>
</figure>
<figure class="right">
<img src="./images/rssi_result_knn.png" alt="">
<figcaption>kNN Results</figcaption>
</figure>
</div>
<h4>RTT Results</h4>
<div class="images">
<figure class="left">
<img src="./images/rtt_gt.png" alt="">
<figcaption>Ground Truth</figcaption>
</figure>
<figure class="middle">
<img src="./images/rtt_result.png" alt="">
<figcaption>Neural Net Results</figcaption>
</figure>
<figure class="right">
<img src="./images/rtt_result_knn.png" alt="">
<figcaption>kNN Results</figcaption>
</figure>
</div>
<h4>PoseNet Results</h4>
<h5>Note: Red = ground truth, Blue = predictions</h5>
<div class="images">
<figure class="left">
<img src="./images/hacklab_1.png" alt="" width="200" height="200">
<img src="./images/hacklab_2.png" alt="" width="200" height="200">
<figcaption>HackLab Dataset</figcaption>
</figure>
<figure class="left">
<img src="./images/desk1_1.png" alt="" width="200" height="200">
<img src="./images/desk1_2.png" alt="" width="200" height="200">
<figcaption>TUM Freiburg Desk 1</figcaption>
</figure>
<figure class="left">
<img src="./images/desk2_1.png" alt="" width="200" height="200">
<img src="./images/desk2_2.png" alt="" width="200" height="200">
<figcaption>TUM Freiburg Desk 2</figcaption>
</figure>
</div>
</a>
<a id="Future_Work">
<div class="container-1400">
<h2>Future Work</h2>
<h4>Exploring Scalability</h4>
<div class="paragraph">
<p> Most of the tests in this work were conducted in a controlled environment (5x10 m). It would be valuable to explore how each of these techniques performs in larger, noisier environments.</p>
</div>
<h4>Combining Techniques</h4>
<div class="paragraph">
<p> All of the solutions presented in this research were tested in isolation. A potential area to look into would be how well these techniques perform with each other. This could potentially lead to the creation of large interdependent indoor localization solutions. </p>
</div>
<h4>Developing Smartphone Data Generation Tools</h4>
<div class="paragraph">
<p> As depth sensors and LIDAR sensors are starting to be integrated into smartphones, like the iPhone 12 Pro, collecting accurate data to train indoor camera relocalization models, like PoseNet, may become easier. One area to look into would be to leverage such sensors along with AR APIs to develop such tools for generating datasets. </p>
<p> An example of an environment scanned with iPhone 12 Pro's LIDAR sensor is below.</p>
</div>
</div>
</a>
<div id="renders">
<button onclick="switchModel()" id="modelBttn"></button>
<script src="https://cdnjs.cloudflare.com/ajax/libs/three.js/r126/three.min.js" integrity="sha512-n8IpKWzDnBOcBhRlHirMZOUvEq2bLRMuJGjuVqbzUJwtTsgwOgK5aS0c1JA647XWYfqvXve8k3PtZdzpipFjgg==" crossorigin="anonymous"></script>
<script src="https://unpkg.com/three@0.126.0/examples/js/loaders/GLTFLoader.js"></script>
<script src="https://unpkg.com/three@0.126.0/examples/js/controls/OrbitControls.js"></script>
<script>
const scene = new THREE.Scene();
const camera = new THREE.PerspectiveCamera( 75, (window.innerWidth) / (window.innerHeight), 0.1, 1000 );
camera.position.y = 0;
camera.position.z = 0;
const renderer = new THREE.WebGLRenderer();
renderer.setSize( window.innerWidth/2, window.innerHeight/2 );
document.body.appendChild( renderer.domElement );
controls = new THREE.OrbitControls( camera, renderer.domElement );
controls.target.set( -1, 0, 0);
const light = new THREE.AmbientLight( 0x404040 ); // soft white light
light.intensity = 3.5;
scene.add( light );
let hackModel, taModel;
let currModelName;
const loader = new THREE.GLTFLoader();
loader.setCrossOrigin('anonymous');
loader.load('./LiDAR/Hacklab.glb', function ( gltf ) {
hackModel = gltf.scene;
hackModel.name = "Hacklab"
currModelName = hackModel.name;
scene.add( gltf.scene );
hackModel.position.x = 2;
hackModel.scale *= 4;
}, undefined, function ( error ) {
console.error( error );
} );
loader.load('./LiDAR/TARoom.glb', function ( gltf ) {
taModel = gltf.scene;
taModel.name = "TA Room"
taModel.position.x = -2;
taModel.position.z = 1;
taModel.scale *= 4;
}, undefined, function ( error ) {
console.error( error );
} );
document.getElementById("modelBttn").innerHTML = "Switch to TA Room";
function switchModel() {
document.getElementById("modelBttn").innerHTML = "Switch to " + currModelName;
if (currModelName == hackModel.name)
{
scene.remove(hackModel)
scene.add(taModel);
currModelName = taModel.name;
}
else
{
scene.remove(taModel)
scene.add(hackModel);
currModelName = hackModel.name;
}
}
const animate = function () {
requestAnimationFrame( animate );
//camera.rotation.y += 0.002;
controls.update();
renderer.render( scene, camera );
};
animate();
</script>
</div>
<div>
<img src="./images/360.png" style="width: 75px; display: block;margin-left: auto; margin-right: auto;">
</div>
<a id="Publication">
<div class="container-1400">
<h2>Publication</h2>
<div class="publication">
<p>Ali Raza, Lazar Lolic, Shahmir Akhter, Michael Liut.</p>
<p><a href="https://ieeexplore.ieee.org/document/9662632">Comparing and Evaluating Indoor Positioning Techniques.</a></p>
<p>2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN 2021), Lloret de Mar, Spain, November 2021.</p>
<p>
<code>
@INPROCEEDINGS{evalindoorpos21, <br>
author = {Raza, Ali and Lolic, Lazar and Akhter, Shahmir and Liut, Michael}, <br>
booktitle = {2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)}, <br>
title = {Comparing and Evaluating Indoor Positioning Techniques}, <br>
year = {2021}, <br>
pages = {1-8}, <br>
doi = {10.1109/IPIN51156.2021.9662632}}
</code>
</p>
</div>
</div>
</a>
<br><br><br><br><br>
</body>
</html>