18532023. METHOD FOR TRAINING A POSE ESTIMATOR simplified abstract (Robert Bosch GmbH)

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METHOD FOR TRAINING A POSE ESTIMATOR

Organization Name

Robert Bosch GmbH

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 18532023 titled 'METHOD FOR TRAINING A POSE ESTIMATOR

Simplified Explanation:

The patent application is about training a pose estimator that 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 obtain estimated poses for sensor measurements, and then training a further pose estimator using an autoencoder to map poses from one format to another.

  • The innovation involves training a pose estimator using multiple initial estimators and an autoencoder.
  • The pose estimator processes sensor measurements to determine the pose of an object.
  • The autoencoder helps map poses from one format to another, improving the accuracy of the estimator.
  • The training process enhances the precision and reliability of the pose estimator.
  • The technology aims to improve the accuracy of pose estimation for various applications.

Potential Applications:

  • Robotics: Precise pose estimation can improve robotic manipulation and navigation.
  • Augmented Reality: Accurate object pose estimation is crucial for AR applications.
  • Autonomous Vehicles: Pose estimation can enhance object detection and tracking for self-driving cars.
  • Medical Imaging: Improved pose estimation can aid in medical image analysis and diagnostics.
  • Virtual Reality: Accurate pose estimation is essential for realistic VR experiences.

Problems Solved:

  • Inaccurate pose estimation from sensor measurements.
  • Difficulty in mapping poses between different formats.
  • Lack of precision in determining object poses.
  • Limited reliability of initial pose estimators.
  • Challenges in training pose estimators effectively.

Benefits:

  • Enhanced accuracy in determining object poses.
  • Improved reliability of pose estimation technology.
  • Better mapping of poses between different formats.
  • Increased precision in sensor measurement analysis.
  • Enhanced performance in various applications requiring pose estimation.

Commercial Applications:

Pose estimation technology can be utilized in industries such as robotics, augmented reality, autonomous vehicles, medical imaging, and virtual reality to improve accuracy and efficiency in various tasks.

Questions about Pose Estimation Technology: 1. How does the autoencoder contribute to improving pose estimation accuracy? 2. What are the main challenges in training a pose estimator effectively?


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.

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