公开(公告)号：US12288363B2

公开(公告)日：2025-04-29

申请号：US18166118

申请日：2023-02-08

Applicant: NVIDIA Corporation

Inventor： Ayon Sen ,  Gang Pan ,  Cheng-Chieh Yang ,  Yue Wu

IPC: G06T7/80 ,  G01S17/86 ,  G01S17/89 ,  G01S17/931 ,  H04N17/00

Abstract: In various examples, sensor configuration for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that may use image feature correspondences between camera images along with an assumption that image features are locally planar to determine parameters for calibrating an image sensor with a LiDAR sensor and/or another image sensor. In some examples, an optimization problem is constructed that attempts to minimize a geometric loss function, where the geometric loss function encodes the notion that corresponding image features are views of a same point on a locally planar surface (e.g., a surfel or mesh) that is constructed from LiDAR data generated using a LiDAR sensor. In some examples, performing such processes to determine the calibration parameters may remove structure estimation from the optimization problem.

公开(公告)号：US20250022175A1

公开(公告)日：2025-01-16

申请号：US18349779

申请日：2023-07-10

Applicant: NVIDIA Corporation

Inventor： Yue Wu ,  Cheng-Chieh Yang ,  Kang Wang ,  Ayon Sen ,  Hsin Miao

IPC: G06T7/80 ,  G06T7/73 ,  H04N13/246

Abstract: In various examples, sensor calibration for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that calibrate image sensors, such as cameras, using images captured by the image sensors at different time instances. For instance, a first image sensor may generate first image data representing at least two images and a second image sensor may generate second image data representing at least one image. One or more feature points may then be tracked between the images represented by the first image data and the image represented by the second image data. Additionally, the feature point(s), timestamps associated with the images, poses associated with image sensors (e.g., poses of a vehicle), and/or other information may be used to determine one or more values of one or more parameters that calibrate the first image sensor with the second image sensor.

公开(公告)号：US20240161341A1

公开(公告)日：2024-05-16

申请号：US18166118

申请日：2023-02-08

Applicant: NVIDIA Corporation

Inventor： Ayon Sen ,  Gang Pan ,  Cheng-Chieh Yang ,  Yue Wu

IPC: G06T7/80

CPC classification number: G06T7/80 ,  G06T2207/10028 ,  G06T2207/20084

Abstract: In various examples, sensor configuration for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that may use image feature correspondences between camera images along with an assumption that image features are locally planar to determine parameters for calibrating an image sensor with a LiDAR sensor and/or another image sensor. In some examples, an optimization problem is constructed that attempts to minimize a geometric loss function, where the geometric loss function encodes the notion that corresponding image features are views of a same point on a locally planar surface (e.g., a surfel or mesh) that is constructed from LiDAR data generated using a LiDAR sensor. In some examples, performing such processes to determine the calibration parameters may remove structure estimation from the optimization problem.

公开(公告)号：US20240161342A1

公开(公告)日：2024-05-16

申请号：US18166121

申请日：2023-02-08

Applicant: NVIDIA Corporation

Inventor： Ayon Sen ,  Gang Pan ,  Cheng-Chieh Yang ,  Yue Wu

IPC: G06T7/80 ,  G01S17/86 ,  G01S17/89 ,  G01S17/931 ,  H04N17/00

CPC classification number: G06T7/80 ,  G01S17/86 ,  G01S17/89 ,  G01S17/931 ,  H04N17/002 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30244

Abstract: In various examples, sensor configuration for autonomous or semi-autonomous systems and applications is described. Systems and methods are disclosed that may use image feature correspondences between camera images along with an assumption that image features are locally planar to determine parameters for calibrating an image sensor with a LiDAR sensor and/or another image sensor. In some examples, an optimization problem is constructed that attempts to minimize a geometric loss function, where the geometric loss function encodes the notion that corresponding image features are views of a same point on a locally planar surface (e.g., a surfel or mesh) that is constructed from LiDAR data generated using a LiDAR sensor. In some examples, performing such processes to determine the calibration parameters may remove structure estimation from the optimization problem.