OxIOD (Oxford Inertial Odometry Dataset)

Introduced by Chen et al. in OxIOD: The Dataset for Deep Inertial Odometry

OxIOD Dataset

Oxford Inertial Odometry Dataset [1] 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)

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