Robert bosch gmbh (20240212195). METHOD FOR TRAINING A POSE ESTIMATOR simplified abstract
Contents
METHOD FOR TRAINING A POSE ESTIMATOR
Organization Name
Inventor(s)
Istvan Sarandi of Tuebingen (DE)
Alexander Hermans of Aachen (DE)
Bastian Leibe of Herzogenrath (DE)
Timm Linder of Boeblingen (DE)
METHOD FOR TRAINING A POSE ESTIMATOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240212195 titled 'METHOD FOR TRAINING A POSE ESTIMATOR
Simplified Explanation: The patent application describes a method for training a pose estimator, which takes sensor measurements of an object as input and produces the pose of the object as output. The training process involves using multiple initial pose estimators to generate estimated poses for sensor measurements, and then training a further pose estimator on these estimated poses to map poses from one format to another.
Key Features and Innovation:
- Training a pose estimator using multiple initial pose estimators.
- Utilizing an autoencoder trained on estimated poses to map poses from one format to another.
- Enhancing the accuracy and efficiency of pose estimation for objects.
Potential Applications: The technology could be applied in various fields such as robotics, augmented reality, virtual reality, and computer vision for accurate object pose estimation.
Problems Solved: The technology addresses the challenge of accurately estimating the pose of objects from sensor measurements, improving the overall performance and reliability of pose estimation systems.
Benefits:
- Improved accuracy in object pose estimation.
- Enhanced efficiency in processing sensor measurements.
- Versatile applications in different industries.
Commercial Applications: The technology could be used in industries such as manufacturing, healthcare, gaming, and security for tasks requiring precise object pose estimation, leading to improved automation and decision-making processes.
Prior Art: Prior research in the field of computer vision and machine learning may have explored similar techniques for pose estimation using sensor measurements and autoencoders.
Frequently Updated Research: Researchers may be continuously exploring advancements in pose estimation techniques using deep learning models and sensor fusion methods.
Questions about Pose Estimation: 1. How does the use of multiple initial pose estimators improve the accuracy of pose estimation? 2. What are the potential limitations of using autoencoders for mapping poses between different formats?
Original Abstract Submitted
training a pose estimator. the pose estimator may receive as input a sensor measurement representing an object and to produce a pose of the object as output. training the pose estimator may include applying multiple trained initial pose estimators to a pool of sensor measurements to obtain multiple estimated poses for a sensor measurement. a further pose estimator may be trained on multiple training data sets using at least part of an autoencoder trained on the multiple estimated poses to map a pose from a first pose format to a second pose format.
