Google Inc.

Guanfeng Li

Edward Y Chang

Feng Wang

Faen Zhang

Hongji Bao

Qingxuan Yang

Quingxuan Yang

A method and system for providing an indoor map representation are provided herein. Aspects of this disclosure provide a representation of an indoor map environment whereby navigable areas are represented by a series of convex polygons in adjacency relationships with one another. Image analysis may be performed on a floor plan image to identify navigable areas and to determine the boundaries of the polygons, such as by assigning one or more colors to navigable space and one or more colors to non-navigable space. Route-finding operations may be performed using the adjacency relationships to find a path from a source area to a destination area.

Navigating using an indoor map representation

Systems and methods for detecting an attitude of a device are disclosed. The system includes a processing system including at least a sensor processor and an application processor, which are distinct. The system further includes a memory system including one or more computer-readable media. The computer-readable media contains instructions that, if executed by the processing system, cause the system to perform operations. The operations include executing an application using the application processor, and receiving raw sensor readings from one or more sensors, using the sensor processor. The operations also include determining, using the sensor processor, a processed sensor metric comprising attitude data, and transmitting the processed sensor metric to the application.

Hardware attitude detection implementation of mobile devices with MEMS motion sensors

Generating an indoor map model

Systems, methods, and machine-readable media for monitoring the locations of a number of entities are provided. The system may be configured to track the locations of a multitude of entities by grouping entities together into clusters of entities based on their geographic proximity and trajectories. A member of a cluster may be identified as a leader or a follower. The system may receive a number of location reports for the entities and update the locations of the entities based on whether the entity is a leader or a follower. For example, the system may update a back-end location database with the location information for a leader while location updates for followers may be converted into locations that are relative to the corresponding leader's and cached at the system.

Location monitoring using clusters of entities

Techniques are described for obtaining high-resolution physical locations for a wireless device by leveraging the high-resolution physical location capabilities of wireless peers of the wireless device to provide a peer-to-peer location service and facilitate location targeting. Wireless devices provide location updates to a computing cloud, which stores records associating wireless device identifiers with received location information for the corresponding wireless devices. A wireless device issues a peer discovery request to dynamically identify nearby wireless peers in its network. The wireless device then sends wireless device identifiers for the nearby wireless peers in a location request to the computing cloud, which estimates a location for the wireless device using the previously recorded location information for the nearby wireless peers. The computing cloud returns the estimated location to the requesting wireless device.

Peer-to-peer location service

The present application describes a computer-implemented method and system for obtaining position information for a moving mobile device with increased accuracy and reduced power consumption. The subject of the present application combines information from a GPS location sensor with information from MEMS devices such as an acceleration detector and a gyroscope using statistical analysis techniques such as a Kalman filter to estimate the location of the device with greater accuracy while using numerical methods such as the Newton-Raphson Method to minimize power consumption. Minimizing power consumption is possible because GPS signals sampled at a lower rate can conserve power, while GPS sampled at a lower rate and working together with MEMS devices can achieve the same level of location prediction accuracy as a GPS alone sampled at a higher rate.

Growing community of inventors

Fremont, CA, United States of America

Guanfeng Li