Nvidia Corporation

Microsoft Technology Licensing, LLC

Ibrahim Eden

David Nister

Ruchi Bhargava

Joachim Pehserl

Vaibhav Thukral

Tilman Wekel

Michael Kroepfl

Nikolai Smolyanskiy

Ke Chen

Ryan Oldja

David Wehr

Ishwar Kulkarni

Sangmin Oh

Alexander A Popov

Neda Cvijetic

Michael Grabner

Alexey Kamenev

Mudit Agrawal

Jeffrey Liu

Shivkumar Swaminathan

Philippe Bouttefroy

Christopher Brian Quirk

Frederik Schaffalitzky

Vivek Pradeep

Weerapan Wilairat

Anthony Aue

Amir Akbarzadeh

Ruchita Bhargava

Michel Galley

Birgit Henke

Youding Zhu

Ivana Stojanovic

Yu Sheng

Enliang Zheng

Andrew Carley

Vadim Cugunovs

Jordan Marr

Huimin Guo

David Zachris Nister

A deep neural network(s) (DNN) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the DNN. Generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a DNN that includes a common trunk and several heads that predict different outputs. The DNN may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. These outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.

Deep neural network for segmentation of road scenes and animate object instances for autonomous driving applications

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

Top-down object detection from LiDAR point clouds

Segmentation of lidar range images

In various examples, systems and methods are described that generate scene flow in 3D space through simplifying the 3D LiDAR data to '2.5D' optical flow space (e.g., x, y, and depth flow). For example, LiDAR range images may be used to generate 2.5D representations of depth flow information between frames of LiDAR data, and two or more range images may be compared to generate depth flow information, and messages may be passed—e.g., using a belief propagation algorithm—to update pixel values in the 2.5D representation. The resulting images may then be used to generate 2.5D motion vectors, and the 2.5D motion vectors may be converted back to 3D space to generate a 3D scene flow representation of an environment around an autonomous machine.

Belief propagation for range image mapping in autonomous machine applications

LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. Improved techniques for processing the point cloud data that has been collected are provided. The improved techniques include mapping one or more point cloud data points into a depth map, the one or more point cloud data points being generated using one or more sensors; determining one or more mapped point cloud data points within a bounded area of the depth map, and detecting, using one or more processing units and for an environment surrounding a machine corresponding to the one or more sensors, a location of one or more entities based on the one or more mapped point cloud data points.

Analysis of point cloud data using depth maps

In various examples, a deep neural network (DNN) may be used to detect and classify animate objects and/or parts of an environment. The DNN may be trained using camera-to-LiDAR cross injection to generate reliable ground truth data for LiDAR range images. For example, annotations generated in the image domain may be propagated to the LiDAR domain to increase the accuracy of the ground truth data in the LiDAR domain—e.g., without requiring manual annotation in the LiDAR domain. Once trained, the DNN may output instance segmentation masks, class segmentation masks, and/or bounding shape proposals corresponding to two-dimensional (2D) LiDAR range images, and the outputs may be fused together to project the outputs into three-dimensional (3D) LiDAR point clouds. This 2D and/or 3D information output by the DNN may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

Object detection and classification using LiDAR range images for autonomous machine applications

In various examples, locations of directional landmarks, such as vertical landmarks, may be identified using 3D reconstruction. A set of observations of directional landmarks (e.g., images captured from a moving vehicle) may be reduced to 1D lookups by rectifying the observations to align directional landmarks along a particular direction of the observations. Object detection may be applied, and corresponding 1D lookups may be generated to represent the presence of a detected vertical landmark in an image.

Landmark location reconstruction in autonomous machine applications

An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams&#x2014;or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data&#x2014;corresponding to any number of drives by any number of vehicles may be generated and uploaded to the cloud. The mapstreams may be used to generate map data&#x2014;and ultimately a fused high definition (HD) map&#x2014;that represents data generated over a plurality of drives. When localizing to the fused HD map, individual localization results may be generated based on comparisons of real-time data from a sensor modality to map data corresponding to the same sensor modality. This process may be repeated for any number of sensor modalities and the results may be fused together to determine a final fused localization result.

Map creation and localization for autonomous driving applications

Multi-view deep neural network for LiDAR perception

Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improved techniques for processing the point cloud data that has been collected. The improved techniques include mapping 3D point cloud data points into a 2D depth map, fetching a group of the mapped 3D point cloud data points that are within a bounded window of the 2D depth map; and generating geometric space parameters based on the group of the mapped 3D point cloud data points. The generated geometric space parameters may be used for object motion, obstacle detection, freespace detection, and/or landmark detection for an area surrounding a vehicle.

Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improvements for processing the point cloud data that has been collected. The processing improvements include analyzing point cloud data using trajectory equations, depth maps, and texture maps. The processing improvements also include representing the point cloud data by a two dimensional depth map or a texture map and using the depth map or texture map to provide object motion, obstacle detection, freespace detection, and landmark detection for an area surrounding a vehicle.

Analysis of point cloud data using depth and texture maps

Various types of systems or technologies can be used to collect data in a 3D space. For example, LiDAR (light detection and ranging) and RADAR (radio detection and ranging) systems are commonly used to generate point cloud data for 3D space around vehicles, for such functions as localization, mapping, and tracking. This disclosure provides improvements for processing the point cloud data that has been collected. The processing improvements include using a three dimensional polar depth map to assist in performing nearest neighbor analysis on point cloud data for object detection, trajectory detection, freespace detection, obstacle detection, landmark detection, and providing other geometric space parameters.

Analysis of point cloud data using polar depth maps and planarization techniques

Growing community of inventors

Kirkland, WA, United States of America

Ibrahim Eden