Here Global B.v.

Here Gloabl B.v.

Pavel Ivanov

Lauri Aarne Johannes Wirola

Muhammad Irshan Khan

Jari Tapani Syrjärinne

Henri Jaakko Julius Nurminen

Lauri Wirola

Marko Luomi

Tatiana Vyunova

Jari Syrjarinne

Elena-Simona Lohan

Stanislav Krainikov

Artem Vassilyev

Jukka Olavi Talvitie

Henri Nurminen

Jani Käppi

Pasi Pentikainen

Mikko Juhani Blomqvist

Matias Mikkola

Michael Krasniansky

Jari Syrjärinne

Veijo Ilomäki

Jukka Talvitie

Irshan Muhammad Khan

Jari Tapani SYRJÄRINNE

Jari SYRJÄRINNE

Lauri Aarne Wirola

A processor obtains an audio sample captured by an audio sensor associated with a device and/or image data captured by an image sensor associated with the device. The image data comprises a representation of one or more elevator buttons. The processor analyzes the audio sample to identify a floor name indicator in the audio sample and/or the image data to identify an activated elevator button from among the one or more elevator buttons represented by the image data; and determines a floor name based on the floor name indicator identified in the analyzed audio sample and/or a floor name indicator associated with the identified activated elevator button in accordance with the analyzed image data. The processor causes the determined floor name to be stored and/or provided for positioning purposes.

Determining floor name based on audio and/or visual samples

An apparatus obtains a respective identifier communication from a respective mobile apparatus and extracts a respective network interface identifier from the respective identifier communication. The apparatus updates or generates a repository of mobile apparatus identifiers based on the respective network interface identifier. The apparatus filters observation data comprising one or more network interface identifiers based on the repository of mobile apparatus identifiers and/or provides the repository of mobile apparatus identifiers for use in filtering the observation data comprising one or more network interface identifiers.

Generation and use of a repository of mobile apparatus identifiers

An apparatus obtains instances of crowd-sourced data corresponding to a first time period and determines a respective corroboration score based on map data a respective instance of crowd-sourced data. The apparatus determines a first representative corroboration score for the first time period based on respective corroboration scores determined for the instances of crowd-sourced data corresponding to the first time period; and compares the first representative corroboration score for the first time period to a second representative corroboration score corresponding to a second time period, wherein the first time period is different from the second time period. Based on a result of comparing the first and second representative corroboration scores, the apparatus determines whether the instances of crowd-sourced data are potentially manipulated. Responsive to determining that the instances of crowd-sourced data are potentially manipulated, the apparatus performs at least one mitigating action.

Mitigating effects of large-area GNSS spoofing and/or jamming on crowd-sourcing-based map updates

An apparatus obtains a set of radio data comprising signal strength related values for radio signals transmitted by a transmitter with an association of each signal strength related value with a representation of a geographical location. The apparatus applies a frequency transform to the obtained set of radio data to obtain transform coefficients, each transform coefficient comprising a transform index and an associated transform value. The apparatus selects a subset of transform indices having more significant transform values than the remaining transform indices and compresses the transform indices by encoding each transform index exploiting a probability of occurrence of an index value of a respective transform index. The same or another apparatus decodes the compressed transform indices again for use in position operations.

Compressing and decompressing data about radio signals

A network device stores a radio map comprising map data configured to enable radio-based positioning within a geographic area. The radio map is associated with a plurality of instances of quality information, with each instance of quality information corresponding to a respective location within the geographic area. The network device receives a quality information request corresponding to a geographic region within the geographic area. The quality information request is associated with an entity. Based on a determination of whether a contribution by the entity to a portion of the map data that corresponds to the geographic region satisfies one or more significance criteria, the network device either provides or withholds quality information corresponding to the geographic region.

Controlled access of radio map quality information

A computing device, method and computer program product are provided to generate fingerprint data having an obfuscated location reference, thereby maintaining the privacy of the location information provided by fingerprint data and represented by a radio map. For a computing device configured to generate fingerprint data having an obfuscated location reference, the computing device is caused to receive fingerprint data comprising a location reference and measurement(s) of one or more communications nodes. The computing device is caused to determine translation and/or rotation parameter(s) based on a respective identifier of at least one of the one or more communications nodes and to apply one or more of the parameters at least to the location reference, thereby generating an obfuscated location reference. The computing device is also caused to provide the fingerprint data including the obfuscated location reference to another computing device for generation or updating of a radio map.

Computing device and method for generating fingerprint data having an obfuscated location reference

A method, apparatus and computer program product are provided for improving localization of a device. In this regard, a coarse location of the device is determined and a neural network is utilized to determine one or more image-based vectors from the device to respective features in an image captured by an image capture device associated with the device. At one or more location points defined relative to the coarse location of the device, (a) one or more map-based vectors extending from a respective location point to respective features as defined by map data are compared to (b) the one or more image-based vectors. Based on the comparison, a refined location of the device is determined. A method, apparatus and computer program are also provided for training the neural network to determine an image-based vector from the device to a feature in an image captured by the device.

Neural network-based method and apparatus for improving localization of a device

A method, apparatus and computer program product are provided for improving localization of a device. In order to improve the localization, a coarse location of the device is determined and a feature is identified in an image captured by an image capture unit associated with the device. The feature is represented with one or more points or lines within an image plane and a projection is determined of the one or more points or lines from the image plane to a ground plane. At one or more location points defined relative to the coarse location of the device, map data is compared to the projection of the one or more points or lines representative of the feature identified in the image. Based on the comparison, a refined location of the device is determined.

Method and apparatus for improving localization of a device

Disclosed is an approach for improved radio and/or altitude data collection in the context of a positioning system. In particular, processor(s) could execute a first phase of the collection process, which may involve collection of data only in seed area(s) associated with generation of an initial map as well as setting forth guidance, via a user interface of a mobile device, to collect data only in the seed area(s). And when the processor(s) detect a trigger to end the first phase, the processor(s) could responsively execute a second phase of the process, which may involve collection of data in secondary area(s) associated with generation of an updated map as well as setting forth guidance, via the user interface, (i) to collect data in the secondary area(s) and (ii) to visit seed area(s) and/or other area(s) that are deemed to be ready.

Methods and systems for improved data collection in a positioning solution that leverages closed-loop learning (CLL)

A geometric plane defined by a building-perimeter feature in an image capture by a mobile device is identified. The building-perimeter feature is co-located with an exterior wall of a building within which the mobile device is located. An orientation and/or location of the mobile device with respect to the geometric plane and an orientation of the mobile device with respect to a defined geographical direction are determined. A building model for the building and defining perimeter portions of the building (each perimeter portion corresponding to a respective exterior wall) is obtained or accessed. Based on the orientation and/or location of the mobile device with respect to the geometric plane, the orientation of the mobile device with respect to the defined geographical direction, and/or the building model, a particular perimeter portion of the building model that corresponds to the building-perimeter feature is identified and corresponding location information is determined.

Indoor localization based on detection of building-perimeter features

The absolute altitude of multiple floors of a building are determined using a multi-mode learning process. Processor(s) execute application program code to cause an apparatus to operate in an initial floor learning mode that includes capturing outdoor altitude estimates for the apparatus and determining, based in part on the outdoor altitude estimates, instant altitude estimates for an initial floor of the building. An absolute altitude estimate for the initial floor is determined based one the instant altitude estimates. After the absolute altitude estimate for the initial floor satisfies a first threshold certainty, the apparatus operates in a subsequent floor learning mode configured for determining respective absolute altitudes for floors of the building based on the absolute altitude estimate for the initial floor and a priori floor height information for the building, a sensor-based altitude change estimate, and/or a determined floor height for the building.

Multiple mode learning of absolute altitude of floors of a building

Disclosed is an approach to improve data collection for a radio-based positioning system. The disclosed approach may involve processor(s) (e.g., of positioning server(s) and/or of a mobile device) obtaining radio data associated with the mobile device, where the radio data is collected while the mobile device is located within a particular vehicle during a time of commute. The processor(s) may determine a particular location of the particular vehicle representing where the particular vehicle is or was located during the time of commute. And the processor(s) may then use the particular location of the particular vehicle to geo-reference the radio data collected while the mobile device is located within the particular vehicle during the time of commute.

Methods and systems for using a vehicle location to geo-reference radio data collected during a time of commute

A method is provided that is performed by at least one first apparatus with the method including obtaining or causing obtaining information indicative of a variation in intensity of light emitted from at least two light sources positioned along a segment of a road; and obtaining or causing obtaining information indicative of a velocity of the at least one first apparatus based at least on the information indicative of the variation in intensity. A corresponding apparatus is also provided.

Estimation of speed of a vehicle based on light intensity measurements

One or more processors obtain an indication of user input identifying a position of a mobile device within a vehicle. The processor(s) obtain a location of the mobile device with respect to a coordinate system that is independent of the vehicle. The processor(s) determine a location of the vehicle with respect to the coordinate system based at least in part on the location of the mobile device and the identified position of the mobile device within the vehicle. The location of the vehicle with respect to the coordinate system may be determined to sub-meter accuracy.

Growing community of inventors

Tampere, Finland

Pavel Ivanov