Qualcomm Incorporated

The University of Texas System

Saurav Agarwal

Bardia Fallah Behabadi

Casimir Matthew Wierzynski

Aliakbar Aghamohammadi

Sarah Paige Gibson

Christopher G Lott

Kiran Kumar Somasundaram

Gerhard Reitmayr

Serafin Diaz Spindola

Shayegan Omidshafiei

Suman Chakravorty

Described herein are embodiments of a method and system that uses a vertical or upward facing imaging sensor to compute vehicle attitude, orientation, or heading and combines the computed vehicle attitude, orientation, or heading with range bearing measurements from an imaging sensor, LiDAR, sonar, etc., to features in the vicinity of the vehicle to compute accurate position and map estimates.

Method and system for accurate long term simultaneous localization and mapping with absolute orientation sensing

A method of visual navigation for a robot includes integrating a depth map with localization information to generate a three-dimensional (3D) map. The method also includes motion planning based on the 3D map, the localization information, and/or a user input. The motion planning overrides the user input when a trajectory and/or a velocity, received via the user input, is predicted to cause a collision.

Autonomous visual navigation

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.

Stochastic map-aware stereo vision sensor model

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.

Simultaneous mapping and planning by a robot

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College Station, TX, United States of America

Saurav Agarwal