Nvidia Corporation

David Weikersdorfer

Minwoo Park

Sangmin Oh

Claire Delaunay

Emilie Lucie Eloïse Wirbel

Gabriele Pasqualino

Bor-Jeng Chen

Qian Lin

Kenneth William MacLean

Arthur Henry Rajala

Aman Jhunjhunwala

Gregor Markus Kopka

Gyeong Woo Cheon

Sravya Nimmagadda

In various examples, techniques for sensor-fusion based object detection and/or free-space detection using ultrasonic sensors are described. Systems may receive sensor data generated using one or more types of sensors of a machine. In some examples, the systems may then process at least a portion of the sensor data to generate input data, where the input data represents one or more locations of one or more objects within an environment. The systems may then input at least a portion of the sensor data and/or at least a portion of the input data into one or more neural networks that are trained to output one or more maps or other output representations associated with the environment. In some examples, the map(s) may include a height, an occupancy, and/or height/occupancy map generated, e.g., from a birds-eye-view perspective. The machine may use these outputs to perform one or more operations.

Sensor fusion using ultrasonic sensors for autonomous systems and applications

Robot

An autoencoder may be trained to predict 3D pose labels using simulation data extracted from a simulated environment, which may be configured to represent an environment in which the 3D pose estimator is to be deployed. Assets may be used to mimic the deployment environment such as 3D models or textures and parameters used to define deployment scenarios and/or conditions that the 3D pose estimator will operate under in the environment. The autoencoder may be trained to predict a segmentation image from an input image that is invariant to occlusions. Further, the autoencoder may be trained to exclude areas of the input image from the object that correspond to one or more appendages of the object. The 3D pose may be adapted to unlabeled real-world data using a GAN, which predicts whether output of the 3D pose estimator was generated from real-world data or simulated data.

3D pose estimation in robotics

Growing community of inventors

Mountain View, CA, United States of America

David Weikersdorfer