Qualcomm Incorporated

Mandar Narsinh Kulkarni

Jubin Jose

Muryong Kim

Ahmed Kamel Sadek

Techniques and systems are provided for localization of an apparatus. For instance, a process can include: obtaining a map of an environment, the map including a map point representing an object in the environment; obtaining image data including the object in the environment, wherein the image data is associated with a camera pose; obtaining point information from the obtained image data, the obtained point information describing two or more points of the object in the obtained image data; determining whether to associate the point information with the map point based, at least in part, on a comparison of the map point and an estimated point, wherein the estimated point is estimated based on the camera pose and point information; and based on the determination to associate the point information with the map point, associating the point information with the map point to localize an apparatus.

Object aided localization without complete object information

Systems, methods, and devices for radio frequency (RF) ranging-aided localization and crowdsourced mapping are provided. In one aspect, a method performed by a user equipment (UE) includes obtaining sensor data comprising first radio frequency (RF) ranging data and imaging data. The method further includes tagging the first RF ranging data with location information and semantic information, wherein the semantic information is based on the imaging data, and wherein the semantic information indicates a first portion of the RF ranging data is associated with a static object type and a second portion of the RF ranging data is associated with a temporary-static object type different from the static object type. The method further includes transmitting, to a RF ranging assistance server, the first RF ranging data tagged with the location information and the semantic information.

Systems and methods for radio frequency (RF) ranging-aided localization and map generation

Creating and updating an accurate radar map layer for HD map using crowdsourcing may comprise a vehicle obtaining radar data and filtering the radar data on a frame-by-frame basis. In some embodiments, additional filtering may be made on a batch of frames. The vehicle can then transmit the filtered radar data responsive to a determination that a confidence of a position estimate of the vehicle exceeds a conference threshold level and/or a determination that a reliance of the position estimate of the vehicle on the radar data exceeds a reliance threshold level.

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South Plainfield, NJ, United States of America

Mandar Narsinh Kulkarni