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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.
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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.
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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.
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