Here Global B.v.

Nokia Mobile Phones Limited

Nokia Corporation

Nokia Technologies Oy

Marko Luomi

Lauri Aarne Johannes Wirola

Petri Rauhala

Pavel Ivanov

Henri Jaakko Julius Nurminen

Tatiana Vyunova

Mika Viitala

Matti Jokimies

Markus Tapani Hakaste

Petri Järvinen

Vesa Lunden

Henri Nurminen

Lauri Wirola

Teemu Ilmari Savolainen

Pasi Sakari Ojala

Hannu H Kari

Peter Decker

Ari Hourunranta

Petros Belimpasakis

Janne Petteri Bergman

Jouni Kämäräinen

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

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

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, apparatus and computer program product are provided to accelerate the estimation of a radio model of a beamforming access point within a radio environment, such as a beamforming access point that has been newly added to or relocated. In a method, the presence of a first beamforming access point in a radio environment is determined and information is obtained regarding one or more of a plurality of beams that are transmitted by the first access point. The method then estimates the radio model of the first access point based at least upon: (i) at least some of the information regarding the one or more of the beams transmitted by the first access point and (ii) signal propagation characteristics of one or more existing access points in the radio environment as represented by respective radio models of the one or more existing access points.

Method and apparatus for accelerating estimation of a radio model of a beamforming access point

In accordance with the disclosed approach, positioning server(s) could receive, from mobile devices, information indicating a plurality of GNSS rescue areas, each respective GNSS rescue area of the plurality of GNSS rescue areas corresponding to a respective geographic area visited by a respective one of the mobile devices in which (i) at least one GNSS-based position estimate is or was unavailable and (ii) the respective mobile device had demand for positioning data of at least a particular quality level. Given this, the server(s) could generate a GNSS rescue map representing radio data for the plurality of GNSS rescue areas. In this way, the server(s) could transmit, to a mobile device, an offline radio map representing a subset of the GNSS rescue map, to provide radio data for at least one of the GNSS rescue areas or portion thereof.

Offline radio maps for crowdsourced GNSS rescue areas

A method is disclosed that includes associating an identifying information indicative of an item to be tracked from a first device to a second device. The method also includes assigning a master/slave information indicative of a responsibility that the first device and/or the second device is in charge of tracking the item from the first device to the second device. The method further includes determining a position estimate indicative of a position of the item to be tracked based at least partially on the identifying information and the master/slave information. The first device is directly related to the item to be tracked, and the second device is not directly to the item to be tracked. An apparatus, a tangible computer-readable medium and a system are also disclosed.

Tracking of items/assets

A method, apparatus and system are provided for operating one or more drones in a building. In the context of a method, information is determined that includes at least one of real time information or predictive information. The real time information is indicative of a position of at least one individual in the building, while the predictive information is indicative of a predicted location of the at least one individual in the building at a certain time. The method also includes controlling the one or more drones in the building according to the at least one of the real time information or the predictive information to avoid the at least one individual while the drone is performing a task.

Method and apparatus to control one or more drones based on real-time or predictive position information

A method includes obtaining or holding available first radio map information representing a first radio map for a first environment. The method also includes determining, at least partially based on said first radio map information, second radio map information representing a second radio map for a second environment. The second radio map contains or represents a respective radio coverage model for each radio device of a group of radio devices. A portion of the second environment at least partially covers the first environment. A density of radio coverage models contained in or represented by said second radio map in the portion of said second environment and at least partially covering the first environment is higher than a density of radio coverage models contained in or represented by the second radio map in a remaining portion of the second environment. A corresponding apparatus and computer program product are also provided.

Indoor optimized offline radio map

A method is disclosed that includes gathering one or more pieces of crowdsourcing information indicative of one or more positions at which the at least one mobile device is located during the gathering. The method also includes collecting one or more performance indicators indicative of measurable information associated with the gathering of the one or more pieces of crowdsourcing information. The one or more performance indicators enable an evaluation of the gathering of the one or more pieces of crowdsourcing information. The method further includes providing the collected one or more performance indicators. An according apparatus, computer program and system are also disclosed.

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Tampere, Finland

Marko Luomi