Qualcomm Incorporated

Abhishek Tyagi

Mahesh Ramachandran

Murali Chari

Christopher Brunner

Arvind Ramanandan

Mark Leo Moeglein

Hui Chao

Serafin Diaz Spindola

Daniel Knoblauch

Murali Ramaswamy Chari

Embodiments disclosed pertain to the use of user equipment (UE) for the generation of a 3D exterior envelope of a structure based on captured images and a measurement set associated with each captured image. In some embodiments, a sequence of exterior images of a structure is captured and a corresponding measurement set comprising Inertial Measurement Unit (IMU) measurements, wireless measurements (including Global Navigation Satellite (GNSS) measurements) and/or other non-wireless sensor measurements may be obtained concurrently. A closed-loop trajectory of the UE in global coordinates may be determined and a 3D structural envelope of the structure may be obtained based on the closed loop trajectory and feature points in a subset of images selected from the sequence of exterior images of the structure.

Exterior hybrid photo mapping

Embodiments disclosed obtain a plurality of measurement sets from a plurality of sensors in conjunction with the capture of a sequence of exterior and interior images of a structure while traversing locations in and around the structure. Each measurement set may be associated with at least one image. An external structural envelope of the structure is determined from exterior images of the structure and the corresponding outdoor trajectory of a UE. The position and orientation of the structure and the structural envelope is determined in absolute coordinates. Further, an indoor map of the structure in absolute coordinates may be obtained based on interior images of the structure, a structural envelope in absolute coordinates, and measurements associated with the indoor trajectory of the UE during traversal of the indoor area to capture the interior images.

Hybrid photo navigation and mapping

Method, apparatus, and computer program product for merging multiple maps for computer vision based tracking are disclosed. In one embodiment, a method of merging multiple maps for computer vision based tracking comprises receiving a plurality of maps of a scene in a venue from at least one mobile device, identifying multiple keyframes of the plurality of maps of the scene, and merging the multiple keyframes to generate a global map of the scene.

Systems and methods of merging multiple maps for computer vision based tracking

A mobile device tracks a relative pose between a camera and a target using Vision aided Inertial Navigation System (VINS), that includes a contribution from inertial sensor measurements and a contribution from vision based measurements. When the mobile device detects movement of the target, the contribution from the inertial sensor measurements to track the relative pose between the camera and the target is reduced or eliminated. Movement of the target may be detected by comparing vision only measurements from captured images and inertia based measurements to determine if a discrepancy exists indicating that the target has moved. Additionally or alternatively, movement of the target may be detected using projections of feature vectors extracted from captured images.

Adaptive switching between vision aided INS and vision only pose

Vision based tracking of a mobile device is used to remotely control a robot. For example, images captured by a mobile device, e.g., in a video stream, are used for vision based tracking of the pose of the mobile device with respect to the imaged environment. Changes in the pose of the mobile device, i.e., the trajectory of the mobile device, are determined and converted to a desired motion of a robot that is remote from the mobile device. The robot is then controlled to move with the desired motion. The trajectory of the mobile device is converted to the desired motion of the robot using a transformation generated by inverting a hand-eye calibration transformation.

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San Diego, CA, United States of America

Abhishek Tyagi