Abstract: A method performed by an apparatus is described. The method includes receiving map data that is based on first image data, second image data, and a similarity metric. The first image data can be received from a first vehicle and represent an object. The second image data can be received from a second vehicle and represent the object. The similarity metric can be associated with the object represented in the first image data and the object represented in the second image data. The method can also include storing, by a vehicle, the received map data and localizing the vehicle based on the stored map data.

Abstract: A method performed by an apparatus is described. The method includes receiving map data that is based on first image data, second image data, and a similarity metric. The first image data can be received from a first vehicle and represent an object. The second image data can be received from a second vehicle and represent the object. The similarity metric can be associated with the object represented in the first image data and the object represented in the second image data. The method can also include storing, by a vehicle, the received map data and localizing the vehicle based on the stored map data.

Abstract: A method performed by an apparatus is described. The method includes receiving map data that is based on first image data, second image data, and a similarity metric. The first image data can be received from a first vehicle and represent an object. The second image data can be received from a second vehicle and represent the object. The similarity metric can be associated with the object represented in the first image data and the object represented in the second image data. The method can also include storing, by a vehicle, the received map data and localizing the vehicle based on the stored map data.

Abstract: A method performed by an apparatus is described. The method includes receiving a first set of object data corresponding to a first journey. The method also includes receiving a second set of object data corresponding to a second journey. The method further includes determining a similarity metric between the first set of object data and the second set of object data. The similarity metric indicates a distance between the first set of object data and the second set of object data for at least one object. The method additionally includes clustering the first set of object data and the second set of object data for the at least one object based on the similarity metric to produce at least one object cluster. The method also includes producing map data based on the at least one object cluster.

Abstract: A method performed by an apparatus is described. The method includes receiving a set of image frames including at least one object. The method also includes receiving a camera position for each image frame. The method further includes associating the at least one object between image frames based on one or more object points and the received camera position for each image frame to produce two-dimensional (2D) object location data. The method additionally includes estimating three-dimensional (3D) pose data of the at least one object based on the 2D object location data. The method also includes refining the 3D pose data based on a shape constraint.

Abstract: A method for defining a sensor model includes determining a probability of obtaining a measurement from multiple potential causes in a field of view of a sensor modeled based on a stochastic map. The stochastic map includes a mean occupancy level for each voxel in the stochastic map and a variance of the mean occupancy level for each pixel. The method also includes determining a probability of obtaining an image based on the determined probability of obtaining the measurement. The method further includes planning an action for a robot, comprising the sensor, based on the probability of obtaining the image.

Abstract: Methods and apparatuses are described for wireless communications in which various association schemes may be performed for a machine type communication (MTC) device. In a long-term evolution (LTE) heterogeneous network, the MTC device may associate with a macro cell or a small cell using a narrowband MTC channel supported by the cells. Information about the MTC channel, including its frequency spectrum, may be transmitted to the MTC device using reserved bits in a physical broadcast channel (PBCH). Once the MTC device identifies the MTC channel, it may communicate with one or more cells during a frame or during a sub-frame. The MTC device may determine channel metrics of the cells from the MTC communication and may identify a cell with which to associate from the channel metrics. The association may be to a best downlink cell or a best uplink cell based on the operating profile of the MTC device.

Abstract: A method performed by an apparatus is described. The method includes receiving a first set of object data corresponding to a first journey. The method also includes receiving a second set of object data corresponding to a second journey. The method further includes determining a similarity metric between the first set of object data and the second set of object data. The similarity metric indicates a distance between the first set of object data and the second set of object data for at least one object. The method additionally includes clustering the first set of object data and the second set of object data for the at least one object based on the similarity metric to produce at least one object cluster. The method also includes producing map data based on the at least one object cluster.

Abstract: A method performed by an apparatus is described. The method includes receiving a first set of object data corresponding to a first journey. The method also includes receiving a second set of object data corresponding to a second journey. The method further includes determining a similarity metric between the first set of object data and the second set of object data. The similarity metric indicates a distance between the first set of object data and the second set of object data for at least one object. The method additionally includes clustering the first set of object data and the second set of object data for the at least one object based on the similarity metric to produce at least one object cluster. The method also includes producing map data based on the at least one object cluster.

Abstract: Provided are systems and methods for determining timestamp information for High Dynamic Range and/or Wide Dynamic Range composite images. For example, an apparatus is provided that comprises an image sensor configured to capture a plurality of sub-frames of a scene, wherein each sub-frame comprises an image of the scene captured using an exposure time that is different from at least one other exposure time of at least one other sub-frame of the plurality of sub-frames. The apparatus is configured to receive, for each of the plurality of sub-frames, sub-pixel image data corresponding to a first portion of an image frame, determine composite image data corresponding to the first portion of the image frame based on values of the received sub-pixel image data for the plurality of sub-frames, identify an indicator based on the sub-frames corresponding to the received sub-pixel image data, and determine timestamp information, based on the identified indicator.

Abstract: Certain aspects provide a method for wireless communications by a first access point, comprising determining a first schedule of intervals for the first access point to communicate with a first group of one or more wireless devices, wherein intervals of the first schedule are synchronized with wake up or transmission cycles of the first group of one or more wireless devices and communicating with the first group of one or more wireless devices according to the first schedule.

Abstract: Provided are systems and methods for determining timestamp information for High Dynamic Range and/or Wide Dynamic Range composite images. For example, an apparatus is provided that comprises an image sensor configured to capture a plurality of sub-frames of a scene, wherein each sub-frame comprises an image of the scene captured using an exposure time that is different from at least one other exposure time of at least one other sub-frame of the plurality of sub-frames. The apparatus is configured to receive, for each of the plurality of sub-frames, sub-pixel image data corresponding to a first portion of an image frame, determine composite image data corresponding to the first portion of the image frame based on values of the received sub-pixel image data for the plurality of sub-frames, identify an indicator based on the sub-frames corresponding to the received sub-pixel image data, and determine timestamp information, based on the identified indicator.

Abstract: A method substantially simultaneously plans a path and maps an environment by a robot. The method determines a mean of an occupancy level for a location in a map. The method also includes determining a probability distribution function (PDF) of the occupancy level. The method further includes calculating a cost function based on the PDF. Finally, the method includes simultaneously planning the path and mapping the environment based on the cost function.

Abstract: Systems and methods are disclosed for detecting light sources and selectively adjusting exposure times of individual sensors in image sensors. In one aspect, a method includes capturing multiple images of a scene using a digital imager. The method includes generating a blended image by combining the multiple images, and executing an object detection algorithm on the blended image to locate and identify objects. The method includes determining a region of the identified object that contains a light source, and generating bounding box data around the light source region. The method includes communicating the bounding box data to the digital imager and updating the exposure time of the sensors in the bounding box region.

Abstract: Systems and methods are disclosed for detecting light sources and selectively adjusting exposure times of individual sensors in image sensors. In one aspect, a method includes capturing multiple images of a scene using a digital imager. The method includes generating a blended image by combining the multiple images, and executing an object detection algorithm on the blended image to locate and identify objects. The method includes determining a region of the identified object that contains a light source, and generating bounding box data around the light source region. The method includes communicating the bounding box data to the digital imager and updating the exposure time of the sensors in the bounding box region.

Abstract: Systems and methods for dynamic bandwidth management for load-based equipment in unlicensed spectrum are disclosed. In an aspect, the disclosure provides a method for dynamic bandwidth management. The method includes obtaining training data by monitoring a plurality of channels in an unlicensed spectrum during a training period. The method further includes determining that at least a first channel of the plurality of channels is available for a transmission. The method also includes determining, based on the training data, whether to wait for an additional channel of the plurality of channels to become available for the transmission. Determining whether to wait may be based on either training data including probabilities that no additional channel is to become available within a transmission opportunity or a machine learning classification of a current state of the backoff counters based on training data including samples of previous states of backoff counters.

Abstract: Techniques are described for wireless communication. A first method includes contending for access to a first channel of a radio frequency spectrum, and transmitting, upon winning contention for access to the first channel, a first channel reservation indication. The contending may be performed by a first node operating according to a first radio access technology. The first channel reservation indication may be understood by a second node operating according to a second radio access technology.

Abstract: A method performed by an apparatus is described. The method includes receiving a set of image frames including at least one object. The method also includes receiving a camera position for each image frame. The method further includes associating the at least one object between image frames based on one or more object points and the received camera position for each image frame to produce two-dimensional (2D) object location data. The method additionally includes estimating three-dimensional (3D) pose data of the at least one object based on the 2D object location data. The method also includes refining the 3D pose data based on a shape constraint.

Abstract: A method performed by an apparatus is described. The method includes receiving a first set of object data corresponding to a first journey. The method also includes receiving a second set of object data corresponding to a second journey. The method further includes determining a similarity metric between the first set of object data and the second set of object data. The similarity metric indicates a distance between the first set of object data and the second set of object data for at least one object. The method additionally includes clustering the first set of object data and the second set of object data for the at least one object based on the similarity metric to produce at least one object cluster. The method also includes producing map data based on the at least one object cluster.

Abstract: A method for mapping an environment by an electronic device is described. The method includes obtaining a set of sensor measurements. The method also includes determining a set of voxel occupancy probability distributions respectively corresponding to a set of voxels based on the set of sensor measurements. Each of the voxel occupancy probability distributions represents a probability of occupancy of a voxel over a range of occupation densities. The range includes partial occupation densities.