Samsung electronics co., ltd. (20240091950). PROBABILISTIC APPROACH TO UNIFYING REPRESENTATIONS FOR ROBOTIC MAPPING simplified abstract
Contents
- 1 PROBABILISTIC APPROACH TO UNIFYING REPRESENTATIONS FOR ROBOTIC MAPPING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PROBABILISTIC APPROACH TO UNIFYING REPRESENTATIONS FOR ROBOTIC MAPPING - 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
PROBABILISTIC APPROACH TO UNIFYING REPRESENTATIONS FOR ROBOTIC MAPPING
Organization Name
Inventor(s)
Jungseok Hong of Minneapolis MN (US)
Suveer Garg of New York NY (US)
Ibrahim Volkan Isler of Saint Paul MN (US)
PROBABILISTIC APPROACH TO UNIFYING REPRESENTATIONS FOR ROBOTIC MAPPING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240091950 titled 'PROBABILISTIC APPROACH TO UNIFYING REPRESENTATIONS FOR ROBOTIC MAPPING
Simplified Explanation
The present disclosure describes a method for evaluating the reliability of three-dimensional shape predictions using scene representations, semantic information estimation, pointcloud segmentation, metric embeddings, shape pointcloud prediction, confidence score generation, and robot motion control.
- Obtaining a scene representation with one or more images
- Estimating semantic information of partially-observed objects
- Determining segmented pointclouds based on semantic and geometric information
- Creating metric embeddings of segmented pointclouds
- Predicting completed shape pointclouds of partially-observed objects
- Generating confidence scores for each point of the completed shape pointclouds
- Controlling robot motion based on confidence scores
Potential Applications
This technology could be applied in robotics, computer vision, autonomous vehicles, and augmented reality.
Problems Solved
This technology helps in accurately predicting three-dimensional shapes of partially-observed objects, improving object recognition and scene understanding.
Benefits
The benefits of this technology include enhanced reliability of shape predictions, improved robot motion control, and better decision-making in various applications.
Potential Commercial Applications
Potential commercial applications of this technology include industrial automation, warehouse management, security systems, and medical imaging.
Possible Prior Art
One possible prior art could be the use of probabilistic machine learning models for shape prediction in computer vision applications.
What are the limitations of this technology in real-world applications?
Real-world applications of this technology may face challenges related to computational complexity, data accuracy, and real-time processing requirements.
How does this technology compare to existing methods for shape prediction in robotics?
This technology offers a more comprehensive approach to shape prediction by combining semantic and geometric information, leading to more accurate and reliable predictions compared to traditional methods.
Original Abstract Submitted
the present disclosure provides methods, apparatuses, and computer-readable mediums for evaluating a reliability of three-dimensional shape predictions. in some embodiments, a method includes obtaining a scene representation including one or more images, estimating semantic information of partially-observed objects in the scene representation, based on geometric information extracted from the one or more images, determining segmented pointclouds of the partially-observed objects based on the semantic information and the geometric information, creating metric embeddings of the segmented pointclouds corresponding to the partially-observed objects, predicting completed shape pointclouds of the partially-observed objects, generating, using a probabilistic machine learning model, confidence scores for each point of the completed shape pointclouds, based on a correlation between the geometric information and the semantic information, and controlling a motion of a robot based on the confidence scores for each point of the completed shape pointclouds.
