Qualcomm - Greater San Diego Area since Mar 2013
Staff Engineer
Qualcomm 2010 - Feb 2013
Senior Engineer
Google 2008 - 2008
Software Engineer Intern
GE Global Research 2007 - 2007
Graduate Research Intern
Education:
University of Maryland College Park 2003 - 2009
Ph.D, Electrical and Computer Engineering
Indian Institute of Technology, Madras 1999 - 2003
Bachelor of Technology (B.Tech.), Electrical, Electronics and Communications Engineering
P. S. Sr. Sec. School 1987 - 1999
Skills:
Algorithms Analytics Control Theory Machine Learning Computer Vision Image Processing Pattern Recognition Matlab Sensors Signal Processing Opencv R&D Digital Signal Processors Video Processing C++ Perl Distributed Systems Python
Interests:
Quantitative Analysis Video Stabilization and Mosaicking Yoga Structure From Motion Video and Inertial Sensor Fusion Etc Other Finance Computer Vision Stochastic Calculus Research Interests
Bank of America
Vice President - Digital and Mobile and Email Channel and Product Management
Bank of America Feb 2004 - Oct 2011
Vice President - Technology Delivery and Product Quality
Bank of America Feb 2000 - Dec 2004
Senior Qa Manager
J.p. Morgan Aug 1998 - Dec 1999
Qa and Business Analyst
First Data Corporation Oct 1997 - Aug 1998
Senior Business Analyst
Education:
University of California, Berkeley 1994 - 1995
Associates, Project Management
Skills:
Banking E Commerce Process Development Project Budgeting Project Delivery and Execution Resource and Budgeting Budgets Integration Crm Requirements Analysis Process Simulation Quality Assurance Project Delivery Project Management Business Analysis Risk Management Sdlc Agile Methodologies Software Project Management Strategy Testing Management Leadership Team Leadership Vendor Management Project Portfolio Management Mobile Devices Business Process Improvement Process Improvement Business Intelligence It Strategy It Management Analytics Internet Banking
Languages:
English
Us Patents
Framework For Reference-Free Drift-Corrected Planar Tracking Using Lucas-Kanade Optical Flow
Mahesh Ramachandran - San Diego CA, US Ashwin Swaminathan - San Diego CA, US Murali R. Chari - San Diego CA, US Serafin Diaz Spindola - San Diego CA, US
Assignee:
QUALCOMM INCORPORATED - San Diego CA
International Classification:
H04N 5/225 G06K 9/00
US Classification:
3482071, 382103, 348E05024
Abstract:
Reference free tracking of position by a mobile platform is performed using images of a planar surface. Tracking is performed optical flow techniques, such as pyramidal Lucas-Kanade optical flow with multiple levels of resolution, where displacement is determined with pixel accuracy at lower resolutions and at sub-pixel accuracy at full resolution, which improves computation time for real time performance. Periodic drift correction is performed by matching features between a current frame and a keyframe. The keyframe may be replaced with the drift corrected current image.
Christopher Brunner - San Diego CA, US Mahesh Ramachandran - San Diego CA, US Arvind Ramanandan - San Diego CA, US Murali Ramaswamy Chari - San Diego CA, US
Assignee:
QUALCOMM Incorporated - San Diego CA
International Classification:
H04N 7/18
US Classification:
348135, 348E07085
Abstract:
A mobile device compensates for a lack of a time stamp when an image frame is captured by estimating the frame time stamp latency. The mobile device captures images frames and time stamps each frame after the frame time stamp latency. A vision based rotation is determined from a pair of frames. A plurality of inertia based rotations is measured using time stamped signals from an inertial sensor in the mobile device based on different possible delays between time stamping each frame and time stamps on the signals from the inertial sensors. The determined rotations may be about the camera's optical axis. The vision based rotation is compared to the plurality of inertia based rotations to determine an estimated frame time stamp latency, which is used to correct the frame time stamp latency when time stamping subsequently captured frames. A median latency determined using different frame pairs may be used.
Adaptive Switching Between Vision Aided Ins And Vision Only Pose
Arvind Ramanandan - San Diego CA, US Christopher Brunner - San Diego CA, US Mahesh Ramachandran - San Diego CA, US Abhishek Tyagi - San Diego CA, US Daniel Knoblauch - San Diego CA, US Murali Ramaswamy Chari - San Diego CA, US
Assignee:
QUALCOMM Incorporated - San Diego CA
International Classification:
G06K 9/00 H04N 7/18
US Classification:
348142, 382103, 348E07085
Abstract:
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.
Mahesh Ramachandran - San Diego CA, US Christopher Brunner - San Diego CA, US Arvind Ramanandan - San Diego CA, US Abhishek Tyagi - San Diego CA, US Murali Ramaswamy Chari - San Diego CA, US
Assignee:
QUALCOMM Incorporated - San Diego CA
International Classification:
G06K 9/00
US Classification:
382153
Abstract:
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.
- San Diego CA, US Christopher Brunner - San Diego CA, US Arvind Ramanandan - San Diego CA, US Mahesh Ramachandran - San Jose CA, US Abhishek Tyagi - San Diego CA, US Murali Ramaswamy Chari - San Diego CA, US
International Classification:
G06T 7/00 G01S 19/14 G06T 7/20
Abstract:
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.
Off-Target Tracking Using Feature Aiding In The Context Of Inertial Navigation
- San Diego CA, US Arvind Ramanandan - San Diego CA, US Mahesh Ramachandran - San Jose CA, US Abhishek Tyagi - San Diego CA, US Murali Ramaswamy Chari - San Diego CA, US
International Classification:
G06K 9/00 G06T 7/60
US Classification:
348135
Abstract:
A Visual Inertial Tracker (VIT), such as a Simultaneous Localization And Mapping (SLAM) system based on an Extended Kalman Filter (EKF) framework (EKF-SLAM) can provide drift correction in calculations of a pose (translation and orientation) of a mobile device by obtaining location information regarding a target, obtaining an image of the target, estimating, from the image of the target, measurements relating to a pose of the mobile device based on the image and location information, and correcting a pose determination of the mobile device using an EKF, based, at least in part, on the measurements relating to the pose of the mobile device.
- San Diego CA, US Christopher Brunner - San Diego CA, US Hui Chao - San Jose CA, US Murali Ramaswamy Chari - San Diego CA, US Arvind Ramanandan - San Diego CA, US Mahesh Ramachandran - San Jose CA, US Abhishek Tyagi - San Diego CA, US
International Classification:
H04W 4/04 H04W 4/02
US Classification:
4554561
Abstract:
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.
- San Diego CA, US Christopher BRUNNER - San Diego CA, US Arvind RAMANANDAN - San Diego CA, US Mahesh RAMACHANDRAN - San Jose CA, US Abhishek TYAGI - San Diego CA, US Murali Ramaswamy CHARI - San Diego CA, US
International Classification:
G01C 21/00 G06T 7/20 G01S 19/13
US Classification:
701491, 701541, 701532
Abstract:
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.