Nvidia corporation (20240161342). SENSOR CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 SENSOR CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SENSOR CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SENSOR CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Ayon Sen of Santa Clara CA (US)
Cheng-Chieh Yang of Seattle WA (US)
Yue Wu of Mountain View CA (US)
SENSOR CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161342 titled 'SENSOR CALIBRATION FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Simplified Explanation
The patent application describes a method for calibrating an image sensor with a lidar sensor using image feature correspondences and the assumption that image features are locally planar. This method involves constructing an optimization problem to minimize a geometric loss function that encodes the notion that corresponding image features are views of the same point on a locally planar surface constructed from lidar data.
- Explanation of the patent/innovation:
- Method for calibrating an image sensor with a lidar sensor - Uses image feature correspondences and assumes image features are locally planar - Constructs an optimization problem to minimize a geometric loss function - Encodes the idea that corresponding image features are views of the same point on a locally planar surface
Potential Applications
This technology can be applied in autonomous or semi-autonomous systems such as self-driving cars, drones, and robotics for accurate sensor calibration.
Problems Solved
- Accurate calibration of image sensors with lidar sensors - Removal of structure estimation from the optimization problem - Improved accuracy in determining calibration parameters
Benefits
- Enhanced performance of autonomous systems - Increased accuracy in sensor calibration - Reduction in errors and inaccuracies in sensor data
Potential Commercial Applications
Optimal for companies developing autonomous vehicles, drone technology, robotics, and other applications requiring precise sensor calibration.
Possible Prior Art
Prior art may include methods for sensor calibration using image feature correspondences and lidar data, but the specific approach of constructing an optimization problem to minimize a geometric loss function based on the assumption of locally planar image features may be novel.
Unanswered Questions
How does this method compare to traditional sensor calibration techniques?
This article does not provide a direct comparison to traditional sensor calibration techniques, leaving the reader to wonder about the potential advantages or disadvantages of this new method compared to existing practices.
What are the potential limitations or challenges of implementing this calibration method in real-world applications?
The article does not address any potential limitations or challenges that may arise when implementing this calibration method in practical scenarios, leaving room for speculation on the feasibility and scalability of the proposed approach.
Original Abstract Submitted
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.
