Nvidia Corporation

Elena Oleynikova

Alexander James Millane

Elad Mentovich

Dieter Fox

Siddha Ganju

Fabio Tozeto Ramos

Ankur Handa

Siva Kumar Sastry Hari

Nathan Donald Ratliff

Adam Harper Fishman

Emilie Lucie Eloïse Wirbel

Marco Foco

Caelan Reed Garrett

Apparatuses, systems, and techniques to perform collision-free motion generation (e.g., to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.

Collision-free motion generation

In various examples, a 3D representation of an environment may be generated from sensor data, with objects being detected in the environment using the sensor data and stored as items that can be tracked and located within the 3D representation. The 3D representation of the environment and item information may be used to determine (e.g., identify or predict) a location or position of an item within the 3D representation and/or recommend a storage location for the item within the 3D representation. Using a determined location or position, one or more routes to the location through the 3D representation may be determined. Data corresponding to a determined route may be provided to a user and/or device. User preferences, permissions, roles, feedback, historical item data, and/or other data associated with a user may be used to further enhance various aspects of the disclosure.

3D environment reconstruction for persistent object tracking

Approaches for environment reconstruction and path planning for autonomous machine systems and applications are described. An iterative volumetric mapping function for an ego-machine may compute a distance field, and from the distance field derive a cost map representing a volumetric reconstruction of the physical environment around the ego-machine. The cost map may be used for collision avoidance and path planning. The iterative volumetric mapping function may also optionally compute a color integration map and visualization mesh from the distance field that can be used for visualization of the physical environment around the ego-machine. The cost map may be computed as a Euclidean Signed Distance Field (ESDF) and the distance field from which the cost map is computed may include a Truncated Signed Distance Field (TSDF). The distance field, cost map, color integration map and visualization mesh may each be stored in memory as maps of a plurality of map layers.

Environment reconstruction and path planning for autonomous systems and applications

Growing community of inventors

Zurich, Switzerland

Elena Oleynikova