公开(公告)号：US11713978B2

公开(公告)日：2023-08-01

申请号：US17008100

申请日：2020-08-31

Applicant: NVIDIA Corporation

Inventor： Amir Akbarzadeh ,  David Nister ,  Ruchi Bhargava ,  Birgit Henke ,  Ivana Stojanovic ,  Yu Sheng

IPC: G01C21/00 ,  G06N3/02 ,  G01C21/16

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：US20210063199A1

公开(公告)日：2021-03-04

申请号：US17008100

申请日：2020-08-31

Applicant: NVIDIA Corporation

Inventor： Amir Akbarzadeh ,  David Nister ,  Ruchi Bhargava ,  Birgit Henke ,  Ivana Stojanovic ,  Yu Sheng

IPC: G01C21/00 ,  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：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：US20230204383A1

公开(公告)日：2023-06-29

申请号：US18175713

申请日：2023-02-28

Applicant: NVIDIA Corporation

Inventor： Amir Akbarzadeh ,  David Nister ,  Ruchi Bhargava ,  Birgit Henke ,  Ivana Stojanovic ,  Yu Sheng

IPC: G01C21/00 ,  G06N3/02 ,  G01C21/16

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：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

IPC: G01C21/00 ,  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：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.

公开(公告)号：US20250058796A1

公开(公告)日：2025-02-20

申请号：US18446649

申请日：2023-08-09

Applicant: NVIDIA Corporation

Inventor： Vishisht Gupta ,  Amir Akbarzadeh ,  Yu Sheng

IPC: B60W60/00 ,  G01C21/00

Abstract: In various examples, accuracy determinations for localization in autonomous and semi-autonomous systems and applications are described herein. Systems and methods are disclosed that determine one or more errors associated with vehicle localization using various types of sensor data generated using a vehicle. For instance, a first component of the vehicle may use a map and first sensor data to determine an estimated pose of the vehicle. A second component of the vehicle may then determine the error(s) associated with the estimated pose based on both actual motion of the vehicle within the environment, as determined using second sensor data, and comparing features represented by the first sensor data to features represented by the map. In some examples, the second component may further determine information associated with the error(s), such as one or more uncertainties associated with the error(s).

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

IPC: G01C21/00 ,  G06N3/02 ,  G01C21/16

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.