Samsung Electronics Co., Ltd.

Neha Dawar

Yuming Zhu

Songwei Li

Boon Loong Ng

Hyunchul Kim

Yi Yang

Moonseok Kang

Hoang Viet Nguyen

Madhukar Budagavi

Rajan Laxman Joshi

Aditya V Padaki

Moongee Cho

Jianzhong Zhang

Hossein Najaf-Zadeh

Khuong N Nguyen

Mandar Narsinh Kulkarni

Jiho Shin

Vitali Loseu

Siddharth K Advani

Chiyu Zhang

Aditya Vinod Padaki

A method includes obtaining signal information based on wireless signals communicated between an electronic device and a target device. The method also includes obtaining motion information based on movement of the electronic device. The method further includes identifying first location information based on the motion information and the channel information. Additionally, the method includes identifying second location information based on the orientation of the electronic device and the AoA information. The method also includes determining whether the electronic device is in motion. The method further includes determining whether the target device is within the FoV or outside the FoV of the electronic device based in part on the first location information in response to determining that the electronic device is not in motion or the second location information in response to determining that the electronic device is in motion.

Angle of arrival capability in electronic devices with motion sensor fusion

An electronic device includes a Lidar sensor, a radar sensor, and a processor. The processor is configured to identify one or more objects from Lidar scans. The processor is configured to transmit, via the radar sensor, radar signals for object detection based on reflections of the radar signals received by the radar sensor. While the electronic device travels the area, the processor is configured to generate a first map indicating one or more objects within an area based on the Lidar scans and a second map based on the radar signals. The processor is configured to determine whether the second map indicates a missed object at the portion of the first map that is unoccupied. In response to a determination that the second map indicates the missed object, the processor is configured to modify the first map with the missed object.

Apparatus and methods for multi-sensor SLAM systems

A method includes obtaining signal information based on wireless signals communicated between an electronic device and a target device. The method also includes obtaining motion information based on movement of the electronic device. The method further includes identifying first location information based on the signal information, the first location information indicating whether the target device is within a field of view (FoV) of the electronic device. Additionally, the method includes identifying second location information based on the motion information and the signal information, the second location information indicating whether the target device is within the FoV of the electronic device. The method also includes determining that the target device is within the FoV or outside the FoV of the electronic device based on at least one of the first location information or the second location information.

Angle of arrival determination in electronic devices with fused decision from motion

An electronic device includes a first set of sensors configured to generate motion information. The electronic device also includes a second set of sensors configured to receive information from multiple anchors. The electronic device further includes a processor configured to generate a path to drive the electronic device within an area. The processor is configured to receive the motion information. The processor is configured to generate ranging information based on the information that is received. While the electronic device is driven along the path, the processor is configured to identify a location and heading direction within the area of the electronic device based on the motion information. The processor is configured to modify the estimate of location and the heading direction of the electronic device based on the ranging information. The processor is configured to drive within the area according to the path, based on the location and heading direction.

Sensor fusion for localization and path planning

A method includes obtaining signal information based on wireless signals received from an external electronic device via a first antenna pair and a second antenna pair. The first and second antenna pairs are aligned along different axes. The signal information includes channel information, range information, a first angle of arrival (AoA) based on the first antenna pair, and a second AoA based on the second antenna pair. The method also includes generating an initial prediction of a presence of the external electronic device relative to a field of view (FoV) of the electronic device. The method further includes performing a smoothing operation on the range information and the first and second AoA, the smoothing operation generating a predicted location of the external electronic device relative to the FoV of the electronic device. Additionally, the method includes generating, based on the smoothing operation, confidence values associated with the predicted location.

Three-dimensional angle of arrival capability in electronic devices

A method includes obtaining signal information based on wireless signals received from a target electronic device via a first antenna pair and a second antenna pair. The first and second antenna pairs are aligned along different axes. The signal information includes channel information, range information, a first angle of arrival (AoA) based on the first antenna pair, and a second AoA based on the second antenna pair. The method also includes obtaining tagging information that identifies an environment in which the electronic device is located. The method also includes generating encoded information from a memory module based on the tagging information. The method further includes initializing a field of view (FoV) classifier based on the encoded information. Additionally, the method includes determining whether the target electronic device is in a FoV of the electronic device based on the FoV classifier operating on the signal information.

3D angle of arrival capability in electronic devices with adaptability via memory augmentation

A method includes obtaining channel information, range information, and angle of arrival (AoA) information based on wireless signals communicated between an electronic device and an external electronic device. The method also includes generating an initial prediction of a presence of the external electronic device relative to a field of view (FoV) of the electronic device based on the channel information and at least one of the range information or the AoA information. The initial prediction includes an indication of whether the external electronic device is within the FoV or outside the FoV of the electronic device. The method further includes performing, using a tracking filter, a smoothing operation on the range information and the AoA information. Additionally, the method includes determining that the external electronic device is within the FoV of the electronic device based on the AoA information, the smoothed AoA information, and the initial prediction.

Angle of arrival capability in electronic devices

A decoding device, an encoding device and methods for point cloud encoding and decoding are disclosed. The method for decoding includes decoding an attribute frame, a geometry frame, and occupancy map frame from a received bitstream. The attribute and geometry frames include pixels representing points of a 3D point cloud and the occupancy map frame includes values. The method also includes generating a binary occupancy map frame based on comparing the values in the occupancy map frame to a scaling threshold to determine whether the pixels included in the attribute and geometry frames at corresponding positions in the binary occupancy map frame are valid pixels. The method further includes generating the 3D point cloud using the attribute frame, the geometry frame, and the binary occupancy map frame.

Lossy compression of point cloud occupancy maps

A decoding device, an encoding device and a method for point cloud decoding is disclosed. The method includes receiving a bitstream. The method also includes decoding, from the compressed bitstream, a first frame, a second frame, and an occupancy map frame. The first and the second frame include pixels representing points of a 3D point cloud at different depths. The occupancy map frame indicates whether the pixels included in the first frame and the second frame at that position in the occupancy map frame are valid pixels and whether points of the 3D point cloud are positioned between the first frame and the second frame at that position in the occupancy map frame. The method further includes generating the 3D point cloud using the first frame, the second frame, and the occupancy map frame.

Point cloud compression using continuous surface codes

A decoding device, an encoding device and a method for point cloud decoding is disclosed. The method includes receiving a bitstream. The method also includes decoding the bitstream into a geometry frame and a texture frame. The geometry and texture frames represent include pixels representing points of the 3D point cloud from different layers. The method further includes deriving a set of missing geometry values from the pixels in the geometry frame and a set of missing texture values from the pixels in the texture frame. The method additionally includes generating a first set of frames representing geometry based on the geometry frame and the set of missing geometry values and generating a second set of frames representing texture based on the texture frame and the set of missing texture values. The method also includes generating the 3D point cloud using the first and second sets of frames.

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Plano, TX, United States of America

Neha Dawar