Robot Localization Using Self-Occlusion Modeling
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Solution Overview
Problem
Existing localization techniques for robots fail to accurately account for changes in robot configuration and occlusions, leading to misalignment of sensor data and poor localization performance.
Innovation Solution
A method and system that models and accounts for self-occlusions by the robot's own body, excluding occluded sensor data during localization, and uses overlapping data from different configurations to improve alignment and localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If robot sensor data from different configurations is used for localization, then more environmental information is available, but misalignment occurs due to self-occlusions
Solution Approach 1:
The patent segments sensor data into occluded and non-occluded portions by comparing configurations, allowing selective use of valid data segments for localization while discarding corrupted segments that would cause misalignment
Solution Approach 2:
The patent performs preliminary configuration comparison and occlusion identification before localization processing, pre-filtering sensor data to remove self-occluded portions and prevent misalignment in subsequent localization steps
2Measurement precision
If robot configuration changes are accounted for in localization, then data alignment improves, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential configuration parameters (robot pose, arm position) needed for occlusion detection, separating these critical parameters from full sensor data processing to reduce computational burden while maintaining alignment accuracy
Data Source
AI summary
Methods and apparatus for localizing a robot in an environment are provided. The method comprises determining, for a first configuration of the robot at a first time, first sensor data that is not occluded by a portion of the robot, determining, for a second configuration of the robot at a second time, second sensor data that is not occluded by a portion of the robot, determining first overlapping data corresponding to second sensor data that overlaps the first sensor data when the robot is in the first configuration, and localizing the robot in the environment based on the first overlapping data and the second overlapping data.