Robotic Shape Completion With Point-Level Confidence Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic mapping techniques lack the ability to assess the uncertainty of individual points in predicted 3D shapes, making it difficult for robots to perform complex tasks reliably, especially in environments with occlusions and visual obstructions.
Innovation Solution
A method that uses a probabilistic machine learning model to generate confidence scores for each point in completed shape pointclouds by correlating geometric and semantic information, allowing robots to filter predictions and plan motions with known certainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If shape completion is used to predict complete shape of partially-observable objects, then the robot can plan motion to conduct tasks, but the robot cannot assess the uncertainty of each predicted point
Solution Approach 1:
The patent segments the uncertainty assessment to the point-level, where each predicted point in the completed shape is individually evaluated for uncertainty. This is achieved by processing each point through the probabilistic model to generate discrete confidence scores, allowing selective use of high-confidence points for task planning.
Solution Approach 2:
The patent introduces feedback by using confidence scores to guide task planning decisions. The uncertainty information flows back into the motion planning process, allowing the robot to adjust its plans based on the reliability of shape predictions, such as avoiding regions with high uncertainty or acquiring additional views to reduce uncertainty.
2Manufacturing precision
If related mapping techniques are used to optimize reconstruction quality, then geometric accuracy is improved, but uncertainty information for individual points is not provided
Solution Approach 1:
The patent merges deterministic shape completion with probabilistic uncertainty estimation into a unified framework. The shape completion network generates predicted points while the probabilistic model simultaneously estimates confidence scores, combining geometric reconstruction with uncertainty quantification in a single integrated system.
Solution Approach 2:
The patent creates a composite output that combines deterministic shape predictions with probabilistic confidence information. This composite representation includes both the geometric data from shape completion and the uncertainty data from the probabilistic model, enabling downstream tasks to utilize both aspects simultaneously.
3Device complexity
If only scene-level or object-level uncertainty is provided, then computational complexity is reduced, but point-level uncertainty assessment becomes difficult
Solution Approach 1:
The patent segments the uncertainty assessment to the point-level, where each predicted point in the completed shape is individually evaluated for uncertainty. This is achieved by processing each point through the probabilistic model to generate discrete confidence scores, allowing selective use of high-confidence points for task planning.
Solution Approach 2:
The patent introduces metric embeddings as an intermediary representation that bridges the gap between complex point cloud data and uncertainty estimation. By projecting point cloud data into a learned metric space, the system enables efficient probabilistic modeling and confidence score generation without requiring excessive computational resources.
Data Source
AI summary
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.