University of Pennsylvania

Nikolay Asenov Atanasov

Konstantinos Daniilidis

George J Pappas

Alex Zihao Zhu

Sean Laurence Bowman

A method for simultaneous location and mapping (SLAM) includes receiving, by at least one processor, a set of sensor measurements from a movement sensor of a mobile robot and a set of images captured by a camera on the mobile robot as the mobile robot traverses an environment. The method includes, for each image of at least a subset of the set of images, extracting, by the at least one processor, a plurality of detected objects from the image. The method includes estimating, by the at least one processor, a trajectory of the mobile robot and a respective semantic label and position of each detected object within the environment using the sensor measurements and an expectation maximization (EM) algorithm.

Probabilistic data association for simultaneous localization and mapping

A method for implementing a soft data association modeled with probabilities is provided. The association probabilities are computed in an intertwined expectation maximization (EM) scheme with an optical flow computation that maximizes the expectation (marginalization) over all associations. In addition, longer tracks can be enabled by computing the affine deformation with respect to the initial point and using the resulting residual as a measure of persistence. The computed optical flow enables a varying temporal integration that is different for every feature and sized inversely proportional to the length of the optical flow. The results can be seen in egomotion and very fast vehicle sequences.

Growing community of inventors

San Diego, CA, United States of America

Nikolay Asenov Atanasov