-
公开(公告)号：US20230357076A1
公开(公告)日：2023-11-09
申请号：US18311172
申请日：2023-05-02
Applicant: NVIDIA Corporation
Inventor： Michael Kroepfl , Amir Akbarzadeh , Ruchi Bhargava , Viabhav Thukral , Neda Cvijetic , Vadim Cugunovs , David Nister , Birgit Henke , Ibrahim Eden , Youding Zhu , Michael Grabner , Ivana Stojanovic , Yu Sheng , Jeffrey Liu , Enliang Zheng , Jordan Marr , Andrew Carley
IPC: C03C17/36
CPC classification number: C03C17/3607 , C03C17/3639 , C03C17/3644 , C03C17/366 , C03C17/3626 , C03C17/3668 , C03C17/3642 , C03C17/3681 , C03C2217/70 , C03C2217/216 , C03C2217/228 , C03C2217/24 , C03C2217/256 , C03C2217/281 , C03C2217/22 , C03C2217/23 , C03C2218/156
Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—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—and ultimately a fused high definition (HD) map—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.
-
公开(公告)号：US20220237925A1
公开(公告)日：2022-07-28
申请号：US17718721
申请日：2022-04-12
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: 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 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.
-
公开(公告)号：US20210063200A1
公开(公告)日：2021-03-04
申请号：US17007873
申请日：2020-08-31
Applicant: NVIDIA Corporation
Inventor： Michael Kroepfl , Amir Akbarzadeh , Ruchi Bhargava , Vaibhav Thukral , Neda Cvijetic , Vadim Cugunovs , David Nister , Birgit Henke , Ibrahim Eden , Youding Zhu , Michael Grabner , Ivana Stojanovic , Yu Sheng , Jeffrey Liu , Enliang Zheng , Jordan Marr , Andrew Carley
Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—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—and ultimately a fused high definition (HD) map—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.
-
公开(公告)号：US20190266736A1
公开(公告)日：2019-08-29
申请号：US16051263
申请日：2018-07-31
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: 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.
-
公开(公告)号：US12292495B2
公开(公告)日：2025-05-06
申请号：US17655783
申请日：2022-03-21
Applicant: NVIDIA CORPORATION
Inventor： Amir Akbarzadeh , Andrew Carley , Birgit Henke , Si Lu , Ivana Stojanovic , Jugnu Agrawal , Michael Kroepfl , Yu Sheng , David Nister , Enliang Zheng
IPC: G01S13/04 , B60W40/10 , B60W40/12 , B60W60/00 , G01S7/00 , G01S7/40 , G01S13/86 , G01S13/89 , G01S13/931 , G01S17/04 , G01S17/931 , G06T7/73 , G06V10/26 , G06V10/28
Abstract: One or more embodiments of the present disclosure relate to generation of map data. In these or other embodiments, the generation of the map data may include determining whether objects indicated by the sensor data are static objects or dynamic objects. Additionally or alternatively, sensor data may be removed or included in the map data based on determinations as to whether it corresponds to static objects or dynamic objects.
-
Patent Agency Ranking
公开(公告)号：US12189018B2
公开(公告)日：2025-01-07
申请号：US17655778
申请日：2022-03-21
Applicant: NVIDIA CORPORATION
Inventor： Amir Akbarzadeh , Andrew Carley , Birgit Henke , Si Lu , Ivana Stojanovic , Jugnu Agrawal , Michael Kroepfl , Yu Sheng , David Nister , Enliang Zheng , Niharika Arora
IPC: G01S13/04 , B60W40/10 , B60W40/12 , B60W60/00 , G01S7/00 , G01S7/40 , G01S13/86 , G01S13/89 , G01S13/931 , G01S17/04 , G01S17/931 , G06T7/73 , G06V10/26 , G06V10/28
Abstract: One or more embodiments of the present disclosure relate to generating RADAR (RAdio Detection And Ranging) point clouds based on RADAR data obtained from one or more RADAR sensors disposed on one or more ego-machines. In these or other embodiments, the RADAR point clouds may be communicated to a distributed map system that is configured to generate map data based on the RADAR point clouds. In some embodiments of the present disclosure, certain compression operations may be performed on the RADAR point clouds to reduce the amount of data that is communicated from the ego-machines to the map system.
-
公开(公告)号：US11698272B2
公开(公告)日：2023-07-11
申请号：US17007873
申请日：2020-08-31
Applicant: NVIDIA Corporation
Inventor： Michael Kroepfl , Amir Akbarzadeh , Ruchi Bhargava , Vaibhav Thukral , Neda Cvijetic , Vadim Cugunovs , David Nister , Birgit Henke , Ibrahim Eden , Youding Zhu , Michael Grabner , Ivana Stojanovic , Yu Sheng , Jeffrey Liu , Enliang Zheng , Jordan Marr , Andrew Carley
CPC classification number: G01C21/3841 , G01C21/1652 , G01C21/3811 , G01C21/3867 , G01C21/3878 , G01C21/3896 , G06N3/02
Abstract: An end-to-end system for data generation, map creation using the generated data, and localization to the created map is disclosed. Mapstreams—or streams of sensor data, perception outputs from deep neural networks (DNNs), and/or relative trajectory data—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—and ultimately a fused high definition (HD) map—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.
-
公开(公告)号：US11908203B2
公开(公告)日：2024-02-20
申请号：US17718721
申请日：2022-04-12
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
CPC classification number: G06V20/58 , G01S17/89 , G06T7/30 , G06T7/521 , G06T11/001 , G06T15/04 , G06V20/56 , G06V20/582 , G06V20/584 , G06T2207/10028 , G06T2207/30241
Abstract: 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.
-
公开(公告)号：US10769840B2
公开(公告)日：2020-09-08
申请号：US16051219
申请日：2018-07-31
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: 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.
-
公开(公告)号：US20190266779A1
公开(公告)日：2019-08-29
申请号：US16051219
申请日：2018-07-31
Applicant: Nvidia Corporation
Inventor： Ishwar Kulkarni , Ibrahim Eden , Michael Kroepfl , David Nister
Abstract: 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.
-
-
-
-
-
-
-
-
-
Search Results
12
records
(took 0.013 seconds)
