├── LICENSE ├── README.md ├── data ├── static │ ├── dataset-01 │ │ └── iphone │ │ │ └── imu-gyro.csv │ ├── dataset-02 │ │ └── iphone │ │ │ └── imu-gyro.csv │ ├── dataset-03 │ │ └── iphone │ │ │ └── imu-gyro.csv │ └── dataset-04 │ │ └── iphone │ │ └── imu-gyro.csv └── swing │ ├── dataset-01 │ └── iphone │ │ └── imu-gyro.csv │ └── dataset-02 │ └── iphone │ └── imu-gyro.csv ├── fig └── figure.png └── python ├── DCI-training-0.0.2.py ├── dataset.py ├── full.pt ├── labels.csv ├── model.py └── sync-data.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones 2 | 3 | [Santiago Cortés](https://research.aalto.fi/portal/santiago.cortesreina.html) · [Arno Solin](http://arno.solin.fi) · [Juho Kannala](https://users.aalto.fi/~kannalj1/) 4 | 5 | ## Summary 6 | 7 | Strapdown inertial navigation systems are sensitive to the quality of the data provided by the accelerometer and gyroscope. Low-grade IMUs in handheld smart-devices pose a problem for inertial odometry on these devices. We propose a scheme for constraining the inertial odometry problem by complementing non-linear state estimation by a CNN-based deep-learning model for inferring the momentary speed based on a window of IMU samples. 8 | 9 | This repository provides the codes for replicationg the speed regression setup in [1]. Please, if you use this code/data, please cite the original paper presenting it. 10 | 11 | ## Dependencies: 12 | 13 | Ubuntu 16.04 and python 2.7 (including numpy and matplotlib) were used in all the tests. 14 | 15 | The following Python packages were also used 16 | * [pytorch](https://pytorch.org/) (0.3) 17 | * [pandas](https://pandas.pydata.org/) (0.23) 18 | 19 | ## Download and prepare training data 20 | 21 | Download and unzip ADVIO dataset files (see 22 | [ADVIO](https://github.com/AaltoVision/ADVIO)). 23 | 24 | ```bash 25 | cd data 26 | for i in $(seq -f "%02g" 1 23); 27 | do 28 | wget -O advio-$i.zip https://zenodo.org/record/1321157/files/advio-$i.zip 29 | unzip advio-$i.zip 30 | rm advio-$i.zip 31 | done 32 | cd .. 33 | ``` 34 | Synchronize the accelerometer and gyroscope in the ADVIO data. 35 | 36 | ```bash 37 | cd python 38 | python sync-data.py 39 | ``` 40 | 41 | ## Produce results 42 | 43 | ```bash 44 | cd python 45 | python DCI-training-0.0.2.py 46 | 47 | ``` 48 | 49 | ## References 50 | 51 | [1] Santiago Cortés, Arno Solin, and Juho Kannala, “Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones”, *IEEE International Workshop on Machine Learning for Signal Processing (MLSP)*, Aalborg, Denmark, 2018. [[arXiv]](https://arxiv.org/abs/1808.03485) 52 | 53 | 54 | ## License 55 | 56 | This software is distributed under the GNU General Public License (version 3 or later); please refer to the file `LICENSE.txt`, included with the software, for details. 57 | -------------------------------------------------------------------------------- /fig/figure.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AaltoVision/deep-speed-constrained-ins/fa9cdda3e67a84f7678c3b6c49bc1b334216e539/fig/figure.png -------------------------------------------------------------------------------- /python/DCI-training-0.0.2.py: -------------------------------------------------------------------------------- 1 | # Example Implementation 2 | # 3 | # Description: 4 | # train and test DCI network. 5 | # 6 | # Copyright (C) 2018 Santiago Cortes 7 | # 8 | # This software is distributed under the GNU General Public 9 | # Licence (version 2 or later); please refer to the file 10 | # Licence.txt, included with the software, for details. 11 | 12 | import torch 13 | import pandas as pd 14 | import os 15 | import numpy as np 16 | import matplotlib.pyplot as plt 17 | from torch.utils.data import Dataset, DataLoader 18 | from torchvision import transforms, utils 19 | from torch.autograd import Variable 20 | import subprocess 21 | import time 22 | import csv 23 | 24 | #Import python functions. 25 | from dataset import OdometryDataset 26 | from dataset import ToTensor 27 | from model import vel_regressor 28 | 29 | 30 | #set options 31 | load_model = True 32 | save_model = False 33 | train_model= False 34 | 35 | 36 | #add path to used folders 37 | #Advio 38 | folders=[] 39 | for i in [13,15,16,17,1,2,3,5,6,8,9,10,11,12,18,19,20,21,22]: 40 | path= '/advio-'+str(i).zfill(2)+'/' 41 | folders.append(path) 42 | #Extra data 43 | folders.append("/static/dataset-01/") 44 | folders.append("/static/dataset-02/") 45 | folders.append("/static/dataset-03/") 46 | folders.append("/swing/dataset-01/") 47 | 48 | #Load saved motion labels 49 | labs=[] 50 | with open('labels.csv', 'rb') as csvfile: 51 | spamreader = csv.reader(csvfile, delimiter=',', quotechar='|') 52 | for row in spamreader: 53 | labs.append([int(row[0]),int(row[1]),int(row[2]),float(row[3]),]) 54 | 55 | 56 | #visualize labels in sample vector. 57 | ind=0 58 | acc_lab=0 59 | acc_dat=0 60 | data_labels=[] 61 | plt.figure(figsize=(8, 35)) 62 | for idx, folder in enumerate(folders): 63 | #Load one folder at a time 64 | data=OdometryDataset("../data",[folder],transform=ToTensor()) 65 | #Skip last label from previous dataset 66 | while labs[ind][3]==-2: 67 | ind=ind+1 68 | #Find corresponding labels 69 | stay=True 70 | dat=[] 71 | dat.append([-1,0]) 72 | while stay: 73 | tim=labs[ind][3] 74 | tim=np.round(np.floor(tim)*60+(tim-np.floor(tim))*100) 75 | data_length=(2+(data[len(data)]['time'])-data[0]['time'])[0] 76 | if labs[ind][3]==-1: 77 | stay=False 78 | tim=10000 79 | lab=labs[ind][2] 80 | dat.append([tim,lab]) 81 | ind=ind+1 82 | #Make label vector for each sample 83 | label=[] 84 | start=data[0]['time'] 85 | for i in range(0,len(data)): 86 | t=data[i]['time']-start 87 | for j in range(0,len(dat)-1): 88 | if tdat[j][0]: 89 | label.append(dat[j+1][1]) 90 | #plot results 91 | acc_dat=acc_dat+len(data) 92 | acc_lab=acc_lab+len(label) 93 | plt.subplot(23,1,idx+1) 94 | plt.plot(label) 95 | plt.ylim(-1,5) 96 | frame1 = plt.gca() 97 | frame1.axes.get_xaxis().set_visible(False) 98 | plt.yticks([0,1,2,3,4], ['Standing','Walking','Stairs','Escalator','Elevator']) 99 | plt.grid(b=True,axis='y') 100 | data_labels.append(label) 101 | # Create dataset reader. 102 | T=OdometryDataset("../data",folders,transform=ToTensor()) 103 | 104 | 105 | #load pretrained model or create new one. 106 | if load_model: 107 | model= torch.load('./full.pt', map_location=lambda storage, loc: storage) 108 | else: 109 | model=vel_regressor(Nout=1,Nlinear=7440) 110 | 111 | l=[] 112 | val=[] 113 | 114 | #plot velocity and speed. 115 | velo=[] 116 | sp=[] 117 | t=[] 118 | index=(np.round(np.linspace(0,len(T),1000))) 119 | for i in index: 120 | data=T[int(i)] 121 | velo.append(data['gt'].numpy()) 122 | sp.append((data['gt'].norm())) 123 | t.append(data['time']) 124 | plt.figure() 125 | plt.plot(velo) 126 | plt.title('Velocity Vector') 127 | plt.xlabel('sample') 128 | plt.ylabel('Speed (m/s)') 129 | plt.legend(['x','z','y']) 130 | plt.figure() 131 | plt.title('Speed') 132 | plt.xlabel('sample') 133 | plt.ylabel('Speed (m/s)') 134 | plt.plot(sp) 135 | 136 | 137 | 138 | #Configure data loaders and optimizer 139 | learning_rate = 1e-6 140 | loss_fn = torch.nn.MSELoss(size_average=False) 141 | index=np.arange(len(T)) 142 | np.random.shuffle(index) 143 | train=index[1:int(np.floor(len(T)/10*9))] 144 | test=index[int(np.floor(len(T)/10*9)):-1] 145 | #Split training and validation. 146 | training_loader = DataLoader(T, batch_size=10,shuffle=False, num_workers=4,sampler=torch.utils.data.sampler.SubsetRandomSampler(list(train))) 147 | validation_loader = DataLoader(T, batch_size=10,shuffle=False, num_workers=4,sampler=torch.utils.data.sampler.SubsetRandomSampler(list(test))) 148 | #Create secondary loaders 149 | single_train_Loader = DataLoader(T, batch_size=1,shuffle=False, num_workers=1,sampler=torch.utils.data.sampler.SubsetRandomSampler(list(train))) 150 | single_validation_Loader = DataLoader(T, batch_size=1,shuffle=False, num_workers=1,sampler=torch.utils.data.sampler.SubsetRandomSampler(list(test))) 151 | ordered_Loader = DataLoader(T, batch_size=1,shuffle=False, num_workers=1) 152 | #define optimizer. 153 | optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate) 154 | 155 | # Train model 156 | if train_model: 157 | # Epochs 158 | for t in range(100): 159 | ti = time.time() 160 | acc_loss=0; 161 | val_loss=0; 162 | # Train 163 | for i_batch, sample_batched in enumerate(training_loader): 164 | # Sample data. 165 | data=sample_batched 166 | # Forward pass. 167 | y_pred =model(Variable(data['imu'].float())) 168 | # Sample corresponding ground truth. 169 | y=torch.norm(data['gt'],2,1).type(torch.FloatTensor) 170 | # Compute and print loss. 171 | loss = loss_fn(y_pred,Variable(y)) 172 | # Save loss. 173 | acc_loss +=np.sum(loss.data[0]) 174 | # Zero the gradients before running the backward pass. 175 | model.zero_grad() 176 | # Backward pass. 177 | loss.backward() 178 | # Take optimizer step. 179 | optimizer.step() 180 | # Validation 181 | for i_batch, sample_batched in enumerate(validation_loader): 182 | # Sample data. 183 | data=sample_batched 184 | # Forward pass. 185 | y_pred =model(Variable(data['imu'].float())) 186 | vec=data['gt'] 187 | y=torch.norm(data['gt'],2,1).type(torch.FloatTensor) 188 | loss = loss_fn(y_pred,Variable(y)) 189 | val_loss +=np.sum(loss.data[0]) 190 | # Save loss and print status. 191 | l.append(acc_loss/(len(T)*9/10)) 192 | val.append(val_loss/(len(T)/10)) 193 | print(t) 194 | print(l[-1]) 195 | print(val[-1]) 196 | elapsed = time.time() - ti 197 | print(elapsed) 198 | # Plot loss 199 | plt.plot(np.log(np.array(l)),label = 'Training loss') 200 | plt.plot(np.log(np.array(val)),label = 'Validation loss') 201 | 202 | 203 | #save model 204 | if save_model: 205 | torch.save(model,'./full.pt') 206 | 207 | # Load corresponding prediction and ground truth 208 | pred=[] 209 | sp=[] 210 | for i_batch, sample_batched in enumerate(ordered_Loader): 211 | data=sample_batched 212 | pred.append(model(Variable(data['imu'].float())).data[0].numpy()) 213 | vec=data['gt'] 214 | y=torch.norm(data['gt'],2,1).type(torch.FloatTensor) 215 | sp.append(y.type(torch.FloatTensor).numpy()) 216 | 217 | # Plot prediction and ground truth. 218 | print(np.shape((np.asarray(sp)))) 219 | plt.subplot(211) 220 | plt.plot(np.asarray(pred)[:,:]) 221 | plt.ylabel('Speed (m/s)') 222 | plt.title('Prediction') 223 | plt.subplot(212) 224 | plt.plot(np.asarray(sp)[:,0]) 225 | plt.ylabel('ground truth speed') 226 | pred=np.asarray(pred) 227 | sp=np.asarray(sp) 228 | dat_lab=[] 229 | for label in data_labels: 230 | dat_lab=dat_lab+label 231 | 232 | 233 | #Plot scatter of prediction and ground truth with labels. 234 | pred=[] 235 | sp=[] 236 | R=[] 237 | for i_batch, sample_batched in enumerate(ordered_Loader): 238 | data=sample_batched 239 | pred.append(model(Variable(data['imu'].float())).data[0].numpy()) 240 | vec=data['gt'] 241 | y=torch.norm(data['gt'],2,1).type(torch.FloatTensor) 242 | sp.append(y.type(torch.FloatTensor).numpy()) 243 | R.append(np.array(data['range'])) 244 | print(len(dat_lab)) 245 | print(len(sp)) 246 | pred=np.asarray(pred) 247 | sp=np.asarray(sp) 248 | stat=[] 249 | stair=[] 250 | walk=[] 251 | esc=[] 252 | ele=[] 253 | 254 | Rstat=[] 255 | Rstair=[] 256 | Rwalk=[] 257 | Resc=[] 258 | Rele=[] 259 | 260 | #Separte by label 261 | for i in range(0,len(dat_lab)): 262 | if dat_lab[i]==0: 263 | stat.append([sp[i,0],pred[i]]) 264 | Rstat.append(R[i]) 265 | elif dat_lab[i]==1: 266 | walk.append([sp[i,0],pred[i]]) 267 | Rwalk.append(R[i]) 268 | elif dat_lab[i]==2: 269 | stair.append([sp[i,0],pred[i]]) 270 | Rstair.append(R[i]) 271 | elif dat_lab[i]==3: 272 | esc.append([sp[i,0],pred[i]]) 273 | Resc.append(R[i]) 274 | else: 275 | ele.append([sp[i,0],pred[i]]) 276 | Rele.append(R[i]) 277 | msize=3 278 | plt.figure(figsize=(8,8)) 279 | #Scatter plot. 280 | test=np.array(stat) 281 | plt.plot(test[:,0],test[:,1],'r.',label='static',markersize=msize) 282 | test=np.array(stair) 283 | plt.plot(test[:,0],test[:,1],'g.',label='stair',markersize=msize) 284 | test=np.array(walk) 285 | plt.plot(test[:,0],test[:,1],'b.',label='walk',markersize=msize) 286 | test=np.array(esc) 287 | plt.plot(test[:,0],test[:,1],'k.',label='escalator',markersize=msize) 288 | test=np.array(ele) 289 | plt.plot(test[:,0],test[:,1],'y.',label='elevator',markersize=msize) 290 | 291 | plt.plot([0,1.5],[0,1.5],'k') 292 | plt.xlabel('gt (m/s)') 293 | plt.ylabel('prediction (m/s)') 294 | 295 | #plot histograms by label 296 | axes=plt.gca() 297 | axes.set_xlim((0.0,1.5)) 298 | axes.set_ylim([0.0,1.5]) 299 | axes.legend() 300 | #axes.grid(b=True, which='major', color='k', linestyle='--') 301 | bins=np.linspace(0.0,2.0,20) 302 | f=0 303 | plt.figure() 304 | plt.subplot(511) 305 | plt.title('minimum') 306 | plt.ylabel('static') 307 | test=np.array(Rstat) 308 | plt.hist(test[:,f],bins=bins) 309 | plt.subplot(512) 310 | plt.ylabel('stairs') 311 | test=np.array(Rstair) 312 | plt.hist(test[:,f],bins=bins) 313 | plt.subplot(513) 314 | plt.ylabel('walk') 315 | test=np.array(Rwalk) 316 | plt.hist(test[:,f],bins=bins) 317 | plt.subplot(514) 318 | plt.ylabel('escalator') 319 | test=np.array(Resc) 320 | plt.hist(test[:,f],bins=bins) 321 | plt.subplot(515) 322 | plt.ylabel('elevator') 323 | test=np.array(Rele) 324 | plt.hist(test[:,f],bins=bins) 325 | 326 | f=1 327 | plt.figure() 328 | plt.subplot(511) 329 | plt.title('maximum') 330 | plt.ylabel('static') 331 | test=np.array(Rstat) 332 | plt.hist(test[:,f],bins=bins) 333 | plt.subplot(512) 334 | plt.ylabel('stairs') 335 | test=np.array(Rstair) 336 | plt.hist(test[:,f],bins=bins) 337 | plt.subplot(513) 338 | plt.ylabel('walk') 339 | test=np.array(Rwalk) 340 | plt.hist(test[:,f],bins=bins) 341 | plt.subplot(514) 342 | plt.ylabel('escalator') 343 | test=np.array(Resc) 344 | plt.hist(test[:,f],bins=bins) 345 | plt.subplot(515) 346 | plt.ylabel('elevator') 347 | test=np.array(Rele) 348 | plt.hist(test[:,f],bins=bins) 349 | 350 | 351 | 352 | plt.figure() 353 | # Evaluate in unknown data to the network. 354 | nfolders=[] 355 | nfolders.append("/static/dataset-04/") 356 | Test=OdometryDataset("./../data/",nfolders,transform=ToTensor()) 357 | test_Loader = DataLoader(Test, batch_size=1,shuffle=False, num_workers=1) 358 | 359 | pred=[] 360 | sp=[] 361 | t=[] 362 | for i_batch, sample_batched in enumerate(test_Loader): 363 | data=sample_batched 364 | pred.append(model(Variable(data['imu'].float())).data[0].cpu().numpy()) 365 | vec=data['gt'] 366 | #vertical=torch.norm(vec[:,[1]],2,1) 367 | #vertical=vec[:,1] 368 | #horizontal=torch.norm(vec[:,[0,2]],2,1) 369 | #y=torch.stack((vertical,horizontal),1) 370 | y=torch.norm(data['gt'],2,1).type(torch.FloatTensor) 371 | sp.append(y.type(torch.FloatTensor).numpy()) 372 | t.append(data['time']) 373 | plt.subplot(211) 374 | plt.plot(np.asarray(pred)) 375 | plt.ylabel('Predicted speed') 376 | plt.subplot(212) 377 | plt.plot(np.asarray(sp)[:,0]) 378 | plt.ylabel('ground truth speed') 379 | 380 | fig = plt.figure(figsize=(6,6)) 381 | plt.plot(np.asarray(sp)[:,0],np.asarray(pred)[:],'.', label='test data') 382 | plt.plot([0,2],[0,2],'k') 383 | plt.xlabel('gt (m/s)') 384 | plt.ylabel('prediction (m/s)') 385 | 386 | axes=plt.gca() 387 | 388 | axes.set_xlim((0.0,2)) 389 | axes.set_ylim([0.0,2]) 390 | axes.legend() 391 | plt.show() 392 | 393 | 394 | 395 | -------------------------------------------------------------------------------- /python/dataset.py: -------------------------------------------------------------------------------- 1 | # dataset definition 2 | # 3 | # Description: 4 | # Define dataset and import data. 5 | # 6 | # Copyright (C) 2018 Santiago Cortes 7 | # 8 | # This software is distributed under the GNU General Public 9 | # Licence (version 2 or later); please refer to the file 10 | # Licence.txt, included with the software, for details. 11 | 12 | 13 | import torch 14 | import pandas as pd 15 | import numpy as np 16 | import matplotlib.pyplot as plt 17 | from torch.utils.data import Dataset, DataLoader 18 | from torchvision import transforms, utils 19 | from torch.autograd import Variable 20 | import csv 21 | 22 | # Dataset class 23 | class OdometryDataset(Dataset): 24 | def __init__(self, data_folder,datasets, transform=None): 25 | """ 26 | Args: 27 | data_folder (string): Path to the csv file with annotations. 28 | datasets: list of datasets. 29 | transform (callable, optional): Optional transform to be applied 30 | on a sample. 31 | """ 32 | ind=0 33 | self.imu=[] 34 | self.imut=[] 35 | self.pos=[] 36 | self.post=[] 37 | self.limits=[] 38 | self.limits.append(0) 39 | plot=False 40 | #scroll trough folders and attach data. Since there is not that many sequences, one array is used. 41 | for dataset in datasets: 42 | imu_path=data_folder+dataset+"iphone/imu-gyro.csv" 43 | data = pd.read_csv(imu_path,names=list('tlabcdefghijk')) 44 | pos=data[data['l']==7] 45 | imu=data[data['l']==34] 46 | self.imut.append(imu[list('t')]) 47 | self.imu.append(imu[list('abcdef')]) 48 | self.post.append(pos[list('t')]) 49 | self.pos.append(pos[list('bcd')]) 50 | self.transform = transform 51 | self.limits.append(self.limits[ind]+len(self.imu[ind])-300) 52 | 53 | if plot: 54 | plt.plot(self.pos[ind].values) 55 | print(np.shape(np.diff(self.pos[ind].values,axis=0,n=1))) 56 | plt.figure() 57 | dt=np.diff(self.post[ind].values,axis=0) 58 | print(np.shape(dt)) 59 | plt.plot(np.mean((np.diff(self.pos[ind],axis=0)/dt[:,None]),0)) 60 | plt.figure() 61 | plt.plot(self.imu[ind].values) 62 | plt.show() 63 | ind=ind+1 64 | 65 | 66 | # Define the length of the dataset as the number of sequences that can be extracted. 67 | def __len__(self): 68 | 69 | return np.floor((self.limits[-1]-1)/100) 70 | 71 | # read a sample of 100 measurements, avoiding the edges of the diferent captures. 72 | def __getitem__(self, idx): 73 | if idx>len(self): 74 | raise ValueError('Index out of range') 75 | else: 76 | idx=idx*100 77 | 78 | for index in range(0,len(self.limits)): 79 | if idx>=self.limits[index] and idxti]) 97 | inde=np.squeeze(inde) 98 | 99 | 100 | posi=self.pos[dset][inde].values 101 | dt=np.diff(self.post[dset][inde].values,axis=0) 102 | dp=np.diff(posi,axis=0) 103 | T=self.post[dset][inde].values 104 | dT=T[-1]-T[0] 105 | dP=posi[:][-1]-posi[:][0] 106 | 107 | minv=np.min(np.sqrt(np.sum(np.square(dp/dt),axis=1))) 108 | maxv=np.max(np.sqrt(np.sum(np.square(dp/dt),axis=1))) 109 | 110 | gt=np.mean((dp/dt),axis=0) 111 | gt=dP/dT 112 | 113 | 114 | gt=gt.astype('float') 115 | IMU=IMU.astype('float') 116 | sample={'imu':IMU,'gt':gt,'time':T[0],'range':[minv,maxv]} 117 | if self.transform: 118 | sample = self.transform(sample) 119 | 120 | return sample 121 | 122 | # Trasform sample into tensor structure. 123 | class ToTensor(object): 124 | """Convert ndarrays in sample to Tensors.""" 125 | def __call__(self, sample): 126 | imu=sample['imu'] 127 | gt=sample['gt'] 128 | T=sample['time'] 129 | R=sample['range'] 130 | #print(type(R)) 131 | return {'imu': torch.from_numpy(imu),'gt':torch.from_numpy(gt),'time':T,'range':R} 132 | -------------------------------------------------------------------------------- /python/full.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AaltoVision/deep-speed-constrained-ins/fa9cdda3e67a84f7678c3b6c49bc1b334216e539/python/full.pt -------------------------------------------------------------------------------- /python/labels.csv: -------------------------------------------------------------------------------- 1 | 0,0,0,0.05 2 | 0,0,1,0.48 3 | 0,0,2,1.04 4 | 0,0,1,2.05 5 | 0,0,2,2.22 6 | 0,0,0,-1 7 | 0,1,0,-2 8 | 0,2,0,0.05 9 | 0,2,1,0.49 10 | 0,2,0,-1 11 | 0,3,0,0.03 12 | 0,3,1,1.1 13 | 0,3,2,1.25 14 | 0,3,1,1.35 15 | 0,3,2,1.51 16 | 0,3,1,-1 17 | 0,4,0,0.03 18 | 0,4,1,1.52 19 | 0,4,2,2.08 20 | 0,4,1,2.18 21 | 0,4,2,2.31 22 | 0,4,0,-1 23 | 1,0,0,0.17 24 | 1,0,3,0.46 25 | 1,0,1,1.21 26 | 1,0,3,1.43 27 | 1,0,1,2.18 28 | 1,0,3,2.38 29 | 1,0,1,3.16 30 | 1,0,3,3.36 31 | 1,0,1,4.13 32 | 1,0,0,-1 33 | 1,1,0,0.07 34 | 1,1,1,-1 35 | 1,2,0,0.05 36 | 1,2,1,-1 37 | 1,3,0,-2 38 | 1,4,0,0.15 39 | 1,4,2,0.35 40 | 1,4,1,-1 41 | 1,5,0,0.08 42 | 1,5,1,-1 43 | 1,6,0,-2 44 | 1,7,0,0.13 45 | 1,7,1,0.35 46 | 1,7,3,0.5 47 | 1,7,1,1.22 48 | 1,7,3,1.4 49 | 1,7,0,-1 50 | 1,8,0,0.06 51 | 1,8,1,0.28 52 | 1,8,3,0.45 53 | 1,8,1,1.11 54 | 1,8,3,1.28 55 | 1,8,0,-1 56 | 1,9,0,0.05 57 | 1,9,1,-1 58 | 1,10,1,-1 59 | 1,11,1,-1 60 | 2,0,0,0.04 61 | 2,0,1,0.26 62 | 2,0,0,1.03 63 | 2,0,4,1.23 64 | 2,0,1,1.51 65 | 2,0,2,2.1 66 | 2,0,1,2.31 67 | 2,0,2,2.46 68 | 2,0,1,-1 69 | 2,1,1,0.42 70 | 2,1,2,0.57 71 | 2,1,1,1.58 72 | 2,1,2,-1 73 | 3,0,1,-1 74 | 3,1,1,-1 75 | 4,0,1,-1 76 | 4,0,1,-2 77 | 5,0,0,-1 78 | 5,1,0,-1 79 | 5,2,0,-1 80 | 6,0,1,-1 81 | -------------------------------------------------------------------------------- /python/model.py: -------------------------------------------------------------------------------- 1 | # Model definition 2 | # 3 | # Description: 4 | # Define model structure and forward function. 5 | # 6 | # Copyright (C) 2018 Santiago Cortes 7 | # 8 | # This software is distributed under the GNU General Public 9 | # Licence (version 2 or later); please refer to the file 10 | # Licence.txt, included with the software, for details. 11 | 12 | import torch 13 | 14 | # Model 15 | class vel_regressor(torch.nn.Module): 16 | def __init__(self,Nin=6,Nout=1,Nlinear=112*60): 17 | super(vel_regressor, self).__init__() 18 | # Convolutional layers 19 | self.model1 = torch.nn.Sequential( 20 | torch.nn.Conv1d(Nin,60,kernel_size=3,stride=1,groups=Nin), 21 | torch.nn.ReLU(), 22 | torch.nn.Conv1d(60,120,kernel_size=3,stride=1,groups=Nin), 23 | torch.nn.ReLU(), 24 | torch.nn.Conv1d(120,240,kernel_size=3,stride=1), 25 | torch.nn.ReLU(), 26 | torch.nn.MaxPool1d(10, stride=6), 27 | ) 28 | # Fully connected layers 29 | self.model2=model2=torch.nn.Sequential( 30 | torch.nn.Linear(Nlinear, 10*40), 31 | torch.nn.ReLU(), 32 | torch.nn.Linear(10*40, 100), 33 | torch.nn.ReLU(), 34 | torch.nn.Linear(100, Nout) 35 | ) 36 | 37 | 38 | 39 | # Forward pass 40 | def forward(self, x): 41 | x = self.model1(x) 42 | x = x.view(x.size(0), -1) 43 | x = self.model2(x) 44 | return x -------------------------------------------------------------------------------- /python/sync-data.py: -------------------------------------------------------------------------------- 1 | # Synchronize the accelerometer and gyroscope data 2 | # 3 | # Description: 4 | # Create data structure containing IMU and ARKit data. 5 | # 6 | # Copyright (C) 2018 Santiago Cortes 7 | # 8 | # This software is distributed under the GNU General Public 9 | # Licence (version 2 or later); please refer to the file 10 | # Licence.txt, included with the software, for details. 11 | 12 | import pandas as pd 13 | import numpy as np 14 | 15 | for i in range(1,24): 16 | #Read data 17 | path= '../data/advio-'+str(i).zfill(2)+'/iphone/arkit.csv' 18 | arkit=pd.read_csv(path,names=list('tabcdefg')) 19 | path= '../data/advio-'+str(i).zfill(2)+'/iphone/accelerometer.csv' 20 | acc=( pd.read_csv(path,names=list('tabc'))) 21 | path= '../data/advio-'+str(i).zfill(2)+'/iphone/gyro.csv' 22 | gyro=( pd.read_csv(path,names=list('tabc'))) 23 | g=[] 24 | a=[] 25 | t=np.array((map(float,acc[list('t')].values))) 26 | zer=t*0 27 | # Make imu 28 | for c in 'abc': 29 | g.append(np.interp( np.array((map(float,acc[list('t')].values))), np.array((map(float,gyro[list('t')].values))), np.array((map(float,gyro[list(c)].values))))) 30 | a.append(np.array((map(float,acc[list(c)].values)))) 31 | M=np.column_stack((t,zer+34,g[0],g[1],g[2],a[0],a[1],a[2],zer,zer)) 32 | v=[] 33 | t=np.array((map(float,arkit[list('t')].values))) 34 | zer=t*0 35 | #Make arkit data 36 | for c in 'abcdefg': 37 | v.append(np.array((map(float,arkit[list(c)].values)))) 38 | Mkit=np.column_stack((t,zer+7,zer,v[0],v[1],v[2],v[3],v[4],v[5],v[6])) 39 | full=np.concatenate((M,Mkit)) 40 | #sort to time vector 41 | full = full[full[:,0].argsort()] 42 | path= '../data/advio-'+str(i).zfill(2)+'/iphone/imu-gyro.csv' 43 | np.savetxt(path, full, delimiter=",",fmt='%.7f') 44 | --------------------------------------------------------------------------------