Nvidia corporation (20240199068). OBJECT POSE ESTIMATION simplified abstract
Contents
OBJECT POSE ESTIMATION
Organization Name
Inventor(s)
Marco Pavone of San Jose CA (US)
Heng Yang of Cambridge MA (US)
OBJECT POSE ESTIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240199068 titled 'OBJECT POSE ESTIMATION
Simplified Explanation: The patent application describes apparatuses, systems, and techniques to predict keypoints for an object, generate candidate poses based on these predictions, and estimate the object's pose for use in moving a device, such as a robot or autonomous machine.
Key Features and Innovation:
- Prediction of keypoints for an object based on associated data
- Generation of candidate poses for the object
- Estimation of the object's pose for device movement
- Collision-free motion generation using the estimated object pose
- Parallel performance of object pose estimation and motion generation
Potential Applications: This technology can be used in robotics, autonomous machines, and semi-autonomous machines for tasks requiring precise object manipulation and movement.
Problems Solved: This technology addresses the challenges of accurately predicting object poses and generating collision-free motion paths for devices in real-world or virtual environments.
Benefits:
- Improved accuracy in object pose estimation
- Enhanced safety through collision-free motion generation
- Efficient device movement based on estimated object poses
Commercial Applications: Potential commercial applications include industrial automation, warehouse logistics, and virtual reality simulations that require precise object manipulation and movement.
Prior Art: Prior research in computer vision, robotics, and motion planning may provide insights into similar technologies for object pose estimation and motion generation.
Frequently Updated Research: Stay updated on advancements in computer vision, machine learning, and robotics for potential improvements in object pose estimation and motion planning algorithms.
Questions about Object Pose Estimation and Motion Generation: 1. How does this technology improve the efficiency of device movement in real-world applications? 2. What are the key factors influencing the accuracy of object pose estimation in this system?
Original Abstract Submitted
apparatuses, systems, and techniques to obtain prediction set(s) (e.g., region(s)) for keypoint prediction(s) based at least in part on data associated with an object, compute a set of candidate poses for the object based at least in part on the prediction set(s), and estimate an estimated object pose based at least in part on the set of candidate poses. the estimated object pose may be used to move a device. for example the estimated object pose may be used to provide collision-free motion generation for a real-world or virtual device (e.g., a robot, an autonomous machine, or a semi-autonomous machine). in at least one embodiment, at least a portion of the object pose estimation and/or at least a portion of the collision-free motion generation is performed in parallel.
