Determining the location of a mobile device

A computer-implemented method of determining the location of a mobile device comprising a camera. The method comprises the steps of capturing, using the camera, a sequence of images over a period of time; for pairs of consecutive images from the sequence of images, determining, using a first neural network, features indicative of the motion of the device between the time the first image of the pair of images was captured and the time the second image of the pair of images was captured; for a sequence of consecutive images, determining, using a second neural network, features indicative of the location of the device from the features determined by the first neural network; and for a sequence of consecutive images, determining the location of the device from the features determined by the second neural network.

FIELD OF THE INVENTION

The present invention concerns determining the location of a mobile device. More particularly, but not exclusively, the invention concerns using neural networks to determine the location of a mobile device from images captured by a camera of the mobile device.

The invention is particularly, but not exclusively, applicable where the mobile device is an autonomous robot. However, the invention is also applicable to other types of mobile and wearable devices, such as mobile phones, smart watches and the like.

“Location” as discussed herein may refer to absolute location, such as that a mobile device is at a position on the Earth defined by latitude and longitude, and may also refer to relative location with respect to another position (e.g. a mobile device is a distance and direction from an initial starting position). Determination of location also frequently includes determination of orientation, in absolute terms with respect to the Earth's magnetic field for example, and in relative terms as rotation of a certain amount with respect to an initial orientation.

BACKGROUND OF THE INVENTION

It is desirable to be able to determine the location of mobile devices where no GPS signal is available. This is particularly the case for autonomous robots, to allow accurate navigation. A known approach is to use images from a camera in order to determine location. However, such systems tend to require very precise calibration of the camera if location is to be reliably determined. Traditional visual odometry techniques include sparse methods (comprising several steps, including detection and matching of features, motion estimation and optimisation), and direct methods (comprising steps of motion estimation and optimisation). Such techniques tend to require precise camera calibration, and often fail in texture-poor environments (i.e. where there are with few features) or when a camera capturing images is rotated rapidly. In addition, while such systems are usually able to determine the shape of a path of travel, they are usually unable to estimate scale, i.e. the actual distance travelled.

Alternatively, it is known to use a neural network to process the images from a camera in order to determine location. Mohanty et al, DeepVO: A Deep Learning approach for Monocular Visual Odometry, arXiv:1611.06069, published 18 Nov. 2016, discloses such a system. However, there are various problems with known systems that use neural networks. They tend to require training for the specific environments in which they will be used, so cannot be used in new environments without suitable training occurring first.

The present invention seeks to mitigate the above-mentioned problems. Alternatively and/or additionally, the present invention seeks to provide improved methods of determining the location of a mobile device.

SUMMARY OF THE INVENTION

In accordance with a first aspect of the invention there is provided a computer-implemented method of determining the location of a mobile device comprising a camera, comprising the steps of:

capturing, using the camera, a sequence of images over a period of time;

for pairs of consecutive images from the sequence of images, determining, using a first neural network, features indicative of the motion of the device between the time the first image of the pair of images was captured and the time the second image of the pair of images was captured;

for a sequence of consecutive images, determining, using a second neural network, features indicative of the location of the device from the features determined by the first neural network; and

for a sequence of consecutive images, determining the location of the device from the features determined by the second neural network.

By using a combination of two neural networks, it has been found that much more robust and reliable location determination is possible. In particular, the first neural network can be trained to most effectively determine features from the images that are indicative of the motion suggested by the differences between the images, which depends only on the two images and is not dependent upon historical information such as previously determined locations. However, the second neural network can at the same time be trained to most effectively determine the location of the mobile device from the features determined by the first neural network, for which historical information such as previously determined locations is extremely useful. By splitting the processing across two neural networks in this way, training for both step-wise motion and overall location can be effectively achieved. Further, both neural networks can be trained simultaneously by training the system as a whole, and so in particular the first neural network can be trained to determine whatever motion features are best for the operation of the system as a whole, rather than being trained to determine motion features with pre-selected attributes which may not in fact be the optimal type of features to use.

Preferably, the orientation of the device is determined in addition to the location of the device. Thus, the “pose” of the device is determined.

Preferably, the images of the sequence of images are monocular images.

Advantageously, the first neural network is a convolutional neural network. This type of neural network is particularly suited for operating upon data with a large number of parameters such as image data.

Advantageously, the second neural network is a recurrent neural network. In this case, preferably the second neural network is a Long Short-Term Memory neural network. Recurrent neural networks, in particular of Long Short-Term Memory type, are particular suitable for operating upon time-dependent data.

Preferably, the method further comprises the step of determining, from the features determined by the second neural network, relative location and orientation information for the device for each pair of consecutive captured images. In this case, preferably the step of determining the location of the device comprises composing the relative location and orientation information determined from the features determined by the second neural network. In other words, the location of the device is determined odometrically from successive motion estimations.

Advantageously, the method further comprising the step of determining corresponding uncertainty information for the relative location and orientation information for each pair of consecutive captured images. The uncertainty information can be used along with pose information as input to simultaneous localisation and mapping (SLAM) algorithms.

Each image of the sequence of images may have associated with its corresponding location information, and the method may further comprise the step of training the first and second neural networks using the corresponding location information. Preferably, each image also has associated with it orientation information.

The device may be an autonomous robot. The device may alternatively be a mobile telephone, wearable device or any other suitable mobile device.

In accordance with a second aspect of the invention there is provided a mobile device comprising:

a processor;

a camera;

wherein the device is arranged to:

capture, using the camera, a sequence of images over a period of time;

for pairs of consecutive images from the sequence of images, determine, using a first neural network provided by the processor, features indicative of the motion of the device between the time the first image of the pair of images was captured and the time the second image of the pair of images was captured;

for a sequence of consecutive images, determine, using a second neural network provided by the processor, features indicative of the location from the features determined by the first neural network; and for a sequence of consecutive images, determine the location of the device from the features determined by the second neural network.

Preferably, the device is arranged to determine the orientation of the device in addition to the location of the device.

Preferably, the images of the sequence of images are monocular images.

Advantageously, the first neural network is a convolutional neural network.

Advantageously, the second neural network is a recurrent neural network. In this case, preferably the second neural network is a Long Short-Term Memory neural network.

Preferably, the device is further arranged to determine, from the features determined by the second neural network, relative location and orientation information for the device for each pair of consecutive captured images. In this case, preferably the device is arranged to determine the location of the device by composing the relative location and orientation information determined from the features determined by the second neural network.

Advantageously, the device is further arranged to determine corresponding uncertainty information for the relative location and orientation information for each pair of consecutive captured images.

In accordance with a third aspect of the invention there is provided a computer program product arranged, when executed on a mobile device, to perform any of the methods described above.

In accordance with a fourth aspect of the invention there is provided a computer program product arranged, when executed on a mobile device, to provide any of the mobile devices described above.

DETAILED DESCRIPTION

A schematic diagram of an autonomous robot in accordance with an embodiment of the invention is shown inFIG. 1. The autonomous robot1comprises a processor2. It will be appreciated that in different embodiments the processor2may be a single processor system, a dual processor system, or any other suitable processor system. The processor2is in communication with a camera3, and a memory4which stores (amongst other things) images captured by the camera3.

The operation of the autonomous robot1to determine its location is now described with reference to the flowchart ofFIG. 2. The process is also shown schematically inFIG. 3. At each time step the autonomous robot1determines its location based on the information currently available to it.FIG. 3shows three successive time steps t, t+1 and t+2, where for each time step a “pose” for the autonomous robot1is determined, Poset, Poset+1and Poset+2respectively, where a pose is a combination of location and heading information (i.e. the direction in which the autonomous robot is facing).

First, a pair of images consecutive captured by the camera3are obtained (step21, portion31ofFIG. 3). Each image is then pre-processed (step22, portion32ofFIG. 3), by subtracting the mean RGB channel values from an example set of images. The set of images may be those with which the autonomous robot1is trained, for example, as described in detail below. In addition, the images are resized to be a multiple of 64. However, such pre-processing is optional, and in other embodiments does not occur. As can be seen inFIG. 3, for time step t the initial pair of images results in pre-processed images RGBtand RGBt+1, while for time step t+1 the pre-processed images are RGBt+1and RGBt+2, and so on for the other time steps.

A convolutional neural network (CNN), implemented by the processor2and memory4, takes the pair of pre-processed images and uses it to determine features (step23, portion33ofFIG. 3). The CNN determines the features in accordance with its training, which has been performed as described below.

CNNs are a type of neural network that incorporates convolutional layers in the network structure and so, in contrast to fully-connected layers used in other types of neural network, are able to take advantage of the spatial regularity of data. This means that the number of parameters required for the CNN is significantly reduced, allowing them to operate on high-dimensional input (such as raw image data). In CNNs, multiple convolutional operations are applied at each convolutional layer, to determine a number of features from the output map of the previous layer. The filter kernels with which the maps are convolved are learned during training, as described for example in [38].

The CNN takes as input a tensor generated by stacking the pair of pre-processed consecutive images. The CNN is composed of nine convolutional layers, each except the last followed by a Rectified Linear Unit (ReLU) non-linearity activation, giving 17 layers in total. The layers are configured as follows:

The sizes of the receptive fields in the network gradually reduce from 7×7 to 5×5 and then 3×3, to capture small interesting features. Zero-paddings are introduced either to adapt to the configurations of the receptive fields, or to preserve the spatial dimension of the tensor after convolution. The number of the channels, i.e. the number of filters for feature detection, increases to learn various features.

In the present embodiment the CNN has 55 million trainable weighs, but it will be appreciated that in other embodiments different numbers of weights could be used.

The features from the final layer (i.e. Conv6) are then the output of the CNN.

Next, a recurrent neural network (RNN) takes the features generates by the CNN, and determines motion features from them (step24, LSTM box of portion34ofFIG. 3). Similarly to the CNN, the RNN does this in accordance with its training, which is described in detail below.

RNNs are a type of neural network in which the layers operate on the input, but also on delayed versions of the hidden layers and/or output. In this way, RNNs have an internal state that they can use as “memory” to keep track of past inputs and corresponding decisions.

In the present embodiment, an RNN with a Long Short-Term Memory (LTSM) architecture is used (of which various variations exist), as shown inFIG. 4, in which ⊙ denotes element-wise product, and ⊕ denotes addition of two vectors. The contents of the memory cell is stored in ct. The input gate it controls how the input enters into the contents of the memory for the current time-step. The forget gate ftdetermines when the memory cell should be emptied by producing a control signal 0 to 1 which clears the memory cell as needed. Finally, the output gate otdetermines whether the contents of the memory cell should be used at the current time step.

The operation of the RNN is described by the following equations:
it=σ(Wxixt+Whiht−1+Wcict−1+bi)
ft=σ(Wxfxt+Whfht−1+Wcfct−1+bf)
zt=tanh(Wxcxt+Whcht−1+bc)
ct=ft⊙ct−1+it⊙zt
ot=act(wxoxt+Wh0ht−1+Wcoct+b0)
ht=ot⊙tanh(ct)

The parameters Wi,jand bifully parameterise the operation of the RNN, and are learned during training. The recurrent hidden layer allows the network to make use of the temporal regularity of the input data to improve its performance.

While in traditional LSTM models only the hidden state is carried over from the previous time step, in the present embodiment the pose determined for the previous time step is directly fed to the RNN as input. This can be seen inFIG. 3, in which the pose for a time step is fed to the LSTM box for the next time step. The reason for doing this is because, for location estimation, the output is essentially the integration of successive displacements at each time step. Consequently, the determined pose for the previous time step is of particular significance.

In the present embodiment the LSTM has two layers with cells of 2000 units, but it will be appreciated that in other embodiments different numbers of layers and units could be used.

Next, the motion features determined by the RNN (of high dimensionality) are passed to a fully connected layer (step25), which outputs features of low dimensionality (at least 6 for pose, 6 for uncertainty, and potentially more for each if a mixture of Gaussians is used to estimate pose and uncertainty).

Next, the low dimensional features from the fully connected layer are passed to an SE(3) layer (step26, SE3 box of portion34ofFIG. 3). The SE(3) composes successive motion features for each time step, in order to at each time step determine a location (in fact a pose, such as Posetfor time step t) for the autonomous robot1.

SE3 is the special Euclidian group whose elements are transformational matrices, which consist of a rotation from the special orthogonal group SO3 and a translation vector:

Producing transformation estimates belonging to SE3 is not straightforward, as the SO3 component needs to be an orthogonal matrix. However, the Lie Algebra se3 of SE3 can be described by components which are not subject to orthogonality constraints:

Conversion between se3 and SE3 can then be accomplished using the exponential map:
exp:se3→SE3

In an alternative embodiment, quaternionic representations of rotations are used instead of the matrix representations. In particular, the ω components are converted to vector:
wt=[0,ωx,ωy,ωz]
{dot over (q)}t=½wt×qk
qt=qt+dt{dot over (q)}t

Calculating the gradients of these quantities can then be done using only simple linear algebra operations. In addition, the expensive eigenvalue decompensation required to calculate the exponential map is avoided.

Thus, in this way the autonomous robot1uses the images from the camera3to estimate its location, in particular its pose.

Estimating location from successive sensor measurements (i.e. odometry) is inevitably subject to drift. As a result, it is common to use it in tandem with loop-closure, map-matching or pose-graph optimisation methods to create Simultaneous Localisation and Mapping (SLAM) systems. A key aspect of integrating odometry measurements into such systems is the availability of uncertainty estimates.

In order to provide such estimates, the output of the fully connected layer is used (before the SE(3) layer). Estimates produced by the fully connected layer are compared with ground truth pose information from training data, resulting in a distribution of errors in pose (location and orientation). A max likelihood approach is then used to train the prediction of the mixture of Gaussian distributions that represents the uncertainty.

In order to operate, neural networks of course need to be trained, which is done by providing test data and a cost function to be minimised. The training of the CNN and RNN of the autonomous robot1as now described; in fact, both are trained simultaneously.

As discussed above, the system of the present embodiment estimates both pose and uncertainty. The test data will be a sequence of images, with “ground truth” poses (i.e. correct poses) for those images. The cost function for training is composed of two parts, the first part relating to the pose estimate and the second part relating to the uncertainty estimate. For pose estimation, the first part of the cost function trains the system to minimise the difference between the estimated poses and the ground truth poses. For uncertainty estimation, the second part of the cost function trains the system by comparing the output of the neural network with pose labels. The training is then done by back-propagation through time, to adjust the weights of the CNN and RNN to best minimise the result of the cost function.

In this way, it can be seen that the CNN is trained to provide the most appropriate features for input to the RNN, and the RNN is simultaneously trained to most accurately determine the pose (and uncertainty thereof) of the autonomous robot1from those features (and previous determinations). The CNN in particular is not trained to best provide features of any particular type or with any specific properties; rather it is simply trained to provide features which are best for the operation of the system as a whole. However, in certain embodiments, to speed up the initial training process, the CNN is initially trained in isolation (or otherwise provided with weights that have the effect of such training) to provide features indicative of movement between successive images. This provides an initial state for the CNN, which is then further optimally trained as the system is trained as a whole.

While the present invention has been described and illustrated with reference to particular embodiments, it will be appreciated by those of ordinary skill in the art that the invention lends itself to many different variations not specifically illustrated herein.