Abstract: A computer implemented method (400) of locating a plurality of mobile device trajectories (301) relative to each other and a map (1, 1?), the mobile device trajectories (301) comprising a time series of position nodes (303) joined by edges (305), the method (400) comprising: obtaining an input pose graph (311) comprising a plurality of mobile device trajectories (301) and a plurality of first constraints (309) defining the position and orientation of nodes (303) and edges (305) of the trajectories relative to each other; performing a non-linear optimisation process on the input pose graph (311) based on the first constraints (309), to reduce a cost function associated with the first constraints (309), the non-linear optimisation process providing a modified pose graph (313); extracting one or more sub-graphs from the modified pose graph (313), and for each sub-graph, individually processing the sub-graph to map match the nodes (303) and edges (305) of the sub-graph to features defined in the map (1,1?); and g

Abstract: A computer-implemented method of determining a location of a mobile device is provided. The method can include receiving inertial data generated at the mobile device, the inertial data including a plurality of samples taken at different times, segmenting the inertial data into pseudo-independent windows, wherein each pseudo-independent window can include a plurality of the samples and wherein one or more initial states for each pseudo-independent window are treated as unknown, estimating a change in navigation state over each pseudo-independent window using the samples of inertial data, and summing the changes in the navigation states over the pseudo-independent windows so as to determine the location of the mobile device.

Abstract: A computer implemented method of aligning a plurality of map segments having a local reference frame to a reference map having a global reference frame, each map segment overlapping a portion of the area represented by the reference map, the method comprising: for each map segment, independently generating one or more candidate alignments, aligning the map segment to the reference map; for each candidate alignment evaluating a cost function representing the likelihood that the candidate alignment is correct; based on the candidate alignments and associated cost functions, generating one or more candidate solutions, each candidate solution comprising a single candidate alignment for each map segment; for each candidate solution, evaluating a cost function based on at least the cost functions for each candidate alignment included in the candidate solution; and based on evaluation of the cost functions, determining alignment of the plurality of map segments.

Abstract: 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.

Abstract: A computer-implemented method of determining the location of a mobile device comprising a camera and at least one inertial sensor. A sequence of images are captured over a period of time, and a first neural network determines features indicative of the motion of the device from pairs of consecutive images. Data indicative of the motion of the device is captured, and a second neural network determines features indicative of the motion of the device from the data. A third neural network then determines features indicative of the location of the device from the features determined by the first neural network and the second neural network. The location of the device is the determined from the features determined by the third neural network.

Abstract: A computer implemented method (400) of determining a location of one or more transitions (5a-e) on a map and/or the time at which one or more transitions (5a-e) occurs, the one or more transitions (5a-e) made by a set of mobile computing devices (13a-c), from a first zone (2) to a second zone (4), the method (400) comprising: obtaining (402) trajectory data representing a plurality of trajectories (302 a, b) collected from one or more mobile computing devices (13 a-c), at least some of the trajectories (302 a, b) passing through the first zone (2) and/or the second zone (4); positioning (404) the trajectories (302 a, b) in a frame of reference defined relative to the map, wherein at least part of at least some of the trajectories (302 a, b) are positioned based on correspondence with other trajectories (302 a, b); and processing the plurality of trajectories (302 a, b) positioned in the frame of reference defined relative to the map to determine the location and/or time.

Abstract: A method 400 of automatically assigning segments of trajectories 110, 120, 130 to floors of a building 100 comprises receiving 801 data concerning a trajectory from a mobile device 152, the data comprising height data and ambient signal data; segmenting 802 the trajectory using the height data, such that a change in the height data marks an end region of a segment. A change in the height data between adjacent segments is referred to as a height link. The method further comprises calculating 803 similarity values for pairs of segments based on the ambient signal data; using the similarity values, grouping 804 the segments into a plurality of groups based on the ambient signal data; checking 805 for errors in the grouping using the height links; accepting 808a the grouping if the checking does not identify any errors, or re-running 807 the grouping if it does; and, once the grouping is accepted, assigning 809 the groups to corresponding floors.

Abstract: A computer-implemented method of determining a location of a mobile device, the method comprising receiving inertial data generated at the mobile device, the inertial data comprising a plurality of samples taken at different times, segmenting the inertial data into pseudo-independent windows, wherein each pseudo-independent window comprises a plurality of the samples and wherein one or more initial states for each pseudo-independent window are treated as unknown, estimating a change in navigation state over each pseudo-independent window using the samples of inertial data, and summing the changes in the navigation states over the pseudo-independent windows so as to determine the location of the mobile device.

Abstract: A mobile device (100) carried by a user has sensors providing acceleration and orientation information. These are processed to provide trajectory for the device. The processing includes improved methods using different modules (1, 2, 3,5, 6, 7) for classifying the motion (1) and detecting a user's steps (5), tracking the orientation (3) and acceleration (2) of the device (100), estimating the length of the user's steps (6) and estimating the heading (7) of the user. The trajectories are compared to a map and corrected by the constraints provided by the map. Parameters of the methods of producing the trajectories are optimised on the basis of maximising probability of the trajectory found by the map matching given the indicated trajectory.

Abstract: A mobile device (100) caried by a user has sensors providing acceleration and orientation information. These are processed to provide trajectory for the device. The processing includes improved methods using different modules (1, 2, 3,5, 6, 7) for classifying the motion (1) and detecting a user's steps (5), tracking the orientation (3) and acceleration (2) of the device (100), estimating the length of the user's steps (6) and estimating the heading (7) of the user. The trajectories are compared to a map and corrected by the constraints provided by the map. Parameters of the methods of producing the trajectories are optimised on the basis of maximising probability of the trajectory found by the map matching given the indicated trajectory.