OxIOD

OxIOD Dataset

Oxford Inertial Odometry Dataset [<a id="d1" href="#oxiod">1</a>] is a large set of inertial data for inertial odometry which is recorded by smartphones at 100 Hz in indoor environment. The suite consists of 158 tests and covers a distance of over 42 km, with OMC ground track available for 132 tests. Therefore, it does not include pure rotational movements and pure translational movements, which are helpful for systematically evaluating the model's performance under different conditions; however, it covers a wide range of everyday movements.

Due to the different focus, some information (for example, the alignment of the coordinate frames) is not accurately described. In addition, the orientation of the ground trace contains frequent irregularities (e.g., jumps in orientation that are not accompanied by similar jumps in the IMU data). The dataset is available at Link.

How to use OxIOD Dataset

The dataset can be download from here. The Dataset Contains:

24 Handheld Sequences

Total 8821 seconds for 7193 meters.

| data1 | time (s) | distance (m) | | :---- | :------- | :----------- | | seq1 | 376 | 301 | | seq2 | 234 | 177 | | seq3 | 188 | 147 | | seq4 | 216 | 166 | | seq5 | 322 | 264 | | seq6 | 325 | 274 | | seq7 | 141 | 118 | | total | 1802 | 1447 |

| data2 | time (s) | dis (m) | | :---- | :------- | :------ | | seq1 | 326 | 281 | | seq2 | 312 | 264 | | seq3 | 301 | 249 | | total | 939 | 794 |

| data3 | time | dis | | :---- | :--- | :--- | | seq1 | 308 | 251 | | seq2 | 379 | 324 | | seq3 | 609 | 533 | | seq4 | 538 | 467 | | seq5 | 383 | 319 | | total | 2217 | 1894 |

| data4 | time | dis | | :---- | :--- | :--- | | seq1 | 317 | 242 | | seq2 | 322 | 243 | | seq3 | 606 | 476 | | seq4 | 438 | 359 | | seq5 | 350 | 284 | | total | 2033 | 1604 |

| data5 | time | dis | | :---- | :--- | :--- | | seq1 | 310 | 237 | | seq2 | 594 | 466 | | seq3 | 560 | 445 | | seq4 | 366 | 306 | | total | 1830 | 1454 |

11 Pocket Sequences

Total 5622 seconds for 4231 meters.

| data1 | time | dis | | :---- | :--- | :--- | | seq1 | 330 | 284 | | seq2 | 456 | 379 | | seq3 | 506 | 405 | | seq4 | 491 | 387 | | seq5 | 240 | 182 | | total | 2023 | 1637 |

| data2 | time | dis | | :---- | :--- | :--- | | seq1 | 651 | 492 | | seq2 | 559 | 414 | | seq3 | 628 | 429 | | seq4 | 668 | 494 | | seq5 | 470 | 371 | | seq6 | 623 | 494 | | total | 3599 | 2694 |

8 Handbag Sequences

Total 4100 seconds for 3431 meters.

| data1 | time | dis | | :---- | :--- | :--- | | seq1 | 575 | 437 | | seq2 | 570 | 467 | | seq3 | 580 | 466 | | seq4 | 445 | 366 | | total | 2170 | 1736 |

| data2 | time | dis | | :---- | :--- | :--- | | seq1 | 575 | 487 | | seq2 | 560 | 499 | | seq3 | 425 | 381 | | seq4 | 370 | 328 | | total | 1930 | 1695 |

13 Trolley Sequences

Total 4262 seconds for 2685 meters.

| data1 | time | dis | | :---- | :--- | :--- | | seq1 | 447 | 251 | | seq2 | 309 | 169 | | seq3 | 359 | 209 | | seq4 | 599 | 362 | | seq5 | 612 | 374 | | seq6 | 586 | 380 | | seq7 | 274 | 174 | | total | 3186 | 1919 |

| data2 | time | dis | | :---- | :--- | :-- | | seq1 | 156 | 106 | | seq2 | 168 | 118 | | seq3 | 161 | 113 | | seq4 | 163 | 113 | | seq5 | 217 | 158 | | seq6 | 211 | 158 | | total | 1076 | 766 |

8 Slow Walking Sequences

Total 4150 seconds for 2421 meters.

| data1 | time | dis | | :---- | :--- | :--- | | seq1 | 612 | 382 | | seq2 | 603 | 353 | | seq3 | 617 | 341 | | seq4 | 594 | 323 | | seq5 | 606 | 352 | | seq6 | 503 | 331 | | seq7 | 311 | 172 | | seq8 | 304 | 167 | | total | 4150 | 2421 |

7 Running Sequences

Total 3732 seconds for 4356 meters.

| data1 | time | dis | | :---- | :--- | :--- | | seq1 | 691 | 761 | | seq2 | 623 | 719 | | seq3 | 590 | 665 | | seq4 | 603 | 679 | | seq5 | 619 | 766 | | seq6 | 303 | 373 | | seq7 | 303 | 393 | | total | 3732 | 4356 |

26 Multi Devices Sequences

Total 7144 seconds for 5350 meters.

| iPhone 5 | time | dis | | :------- | :--- | :--- | | seq1 | 178 | 150 | | seq2 | 163 | 133 | | seq3 | 160 | 126 | | seq4 | 124 | 100 | | seq5 | 174 | 139 | | seq6 | 167 | 136 | | seq7 | 197 | 150 | | seq8 | 184 | 141 | | seq9 | 184 | 142 | | total | 1531 | 1217 |

| iPhone 6 | time | dis | | :------- | :--- | :--- | | seq1 | 180 | 165 | | seq2 | 184 | 171 | | seq3 | 182 | 168 | | seq4 | 150 | 140 | | seq5 | 183 | 162 | | seq6 | 171 | 155 | | seq7 | 184 | 139 | | seq8 | 185 | 148 | | seq9 | 173 | 133 | | total | 1592 | 1381 |

| nexus 5 | time | dis | | :------ | :--- | :--- | | seq1 | 604 | 452 | | seq2 | 609 | 438 | | seq3 | 605 | 414 | | seq4 | 609 | 403 | | seq5 | 607 | 388 | | seq6 | 607 | 401 | | seq7 | 186 | 130 | | seq8 | 194 | 127 | | total | 4021 | 2752 |

35 Multi Users Sequences

Total 8821 seconds for 9465 meters.

| user 2 | time | dis | | :----- | :--- | :--- | | seq1 | 311 | 284 | | seq2 | 358 | 313 | | seq3 | 390 | 328 | | seq4 | 217 | 172 | | seq5 | 311 | 240 | | seq6 | 256 | 193 | | seq7 | 371 | 296 | | seq8 | 450 | 375 | | seq9 | 264 | 221 | | total | 2928 | 2422 |

| user 3 | time | dis | | :----- | :--- | :--- | | seq1 | 382 | 301 | | seq2 | 318 | 272 | | seq3 | 340 | 295 | | seq4 | 232 | 198 | | seq5 | 214 | 185 | | seq6 | 356 | 289 | | seq7 | 258 | 203 | | total | 2100 | 1743 |

| user 4 | time | dis | | :----- | :--- | :--- | | seq1 | 387 | 367 | | seq2 | 329 | 307 | | seq3 | 305 | 288 | | seq4 | 248 | 229 | | seq5 | 356 | 314 | | seq6 | 293 | 272 | | seq7 | 297 | 260 | | seq8 | 468 | 411 | | seq9 | 435 | 364 | | total | 3118 | 2812 |

| user 5 | time | dis | | :----- | :--- | :--- | | seq1 | 294 | 237 | | seq2 | 305 | 264 | | seq3 | 253 | 211 | | seq4 | 390 | 337 | | seq5 | 300 | 226 | | seq6 | 338 | 284 | | seq7 | 168 | 154 | | seq8 | 410 | 395 | | seq9 | 274 | 250 | | seq10 | 152 | 130 | | total | 2884 | 2488 |

26 Large Scale Sequences

Total 4161 seconds for 3465 meters.

| floor1 | time | dis | | :----- | :--- | :--- | | seq1 | 153 | 142 | | seq2 | 165 | 143 | | seq3 | 158 | 142 | | seq4 | 157 | 145 | | seq5 | 156 | 142 | | seq6 | 156 | 142 | | seq7 | 161 | 144 | | seq8 | 155 | 143 | | seq9 | 160 | 126 | | seq10 | 158 | 143 | | total | 1579 | 1412 |

| floor4 | time | dis | | :----- | :--- | :--- | | seq1 | 160 | 170 | | seq2 | 157 | 153 | | seq3 | 162 | 153 | | seq4 | 118 | 106 | | seq5 | 164 | 153 | | seq6 | 163 | 143 | | seq7 | 169 | 141 | | seq8 | 166 | 153 | | seq9 | 172 | 135 | | seq10 | 169 | 154 | | seq11 | 166 | 152 | | seq12 | 165 | 154 | | seq13 | 165 | 133 | | seq14 | 164 | 153 | | seq15 | 163 | 153 | | seq16 | 159 | 133 | | total | 2582 | 2053 |

In each folder, there is a raw data subfolder and a syn data subfolder, which represent the raw data collection without synchronisation but with high precise timestep, and the synchronised data but without high precise timestep.

The header of files is

vicon (vi.csv)*

1. Time
2. Header
3. translation.x translation.y translation.z
4. rotation.x rotation.y rotation.z rotation.w

Sensors (imu.csv)*

1. Time
2. attitude_roll(radians) attitude_pitch(radians) attitude_yaw(radians)
3. rotation_rate_x(radians/s) rotation_rate_y(radians/s) rotation_rate_z(radians/s)
4. gravity_x(G) gravity_y(G) gravity_z(G)
5. user_acc_x(G) user_acc_y(G) user_acc_z(G)
6. magnetic_field_x(microteslas) magnetic_field_y(microteslas) magnetic_field_z(microteslas)