Multi-View Object Reference Generation for Mobile Robot Interaction
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Solution Overview
Problem
Conventional approaches to generating object references from perception data in robotics are inadequate due to incomplete data collection and lack of spatial context, making it difficult for robots to interact effectively with objects in dynamic environments.
Innovation Solution
Methods that leverage stored information about an object and limited poses, using surface normals and neural networks to generate accurate object references, incorporating algorithms for evaluating candidate references and integrating perception data derivatives to enhance spatial context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional perception data collection methods are used, then data collection is simpler, but the completeness and spatial context of the data are insufficient
Solution Approach 1:
The perception data collection is divided into multiple scanning operations from different positions and angles. The system segments the data collection process into discrete scanning steps, each capturing a portion of the object, then combines these segments to form complete perception data with full spatial context.
Solution Approach 2:
The system transitions from single-viewpoint perception to multi-viewpoint perception by collecting data from multiple spatial dimensions. This dimensional expansion allows the robot to reconstruct three-dimensional object models and understand spatial relationships that cannot be obtained from a single perspective.
2Measurement precision
If multiple scanning positions and angles are used to collect perception data, then spatial context and object understanding are improved, but the time and complexity of data collection increase
Solution Approach 1:
The system performs preliminary scanning operations to collect perception data from multiple positions before final object reference generation. By preparing the perception data in advance through multiple scans, the system establishes a comprehensive spatial understanding that enables accurate and efficient object referencing without time pressure during execution.
Solution Approach 2:
The system creates multiple virtual representations (copies) of the object from different scanning perspectives. These copied views are processed and integrated to form a complete three-dimensional model, allowing the robot to understand the object's spatial properties without physically manipulating it multiple times.
3Reliability
If perception data is collected without structured approaches, then data collection is faster, but the ability to generate accurate object references is reduced
Solution Approach 1:
The system employs dynamic scanning strategies that adapt to the object's characteristics and the robot's position. The scanning process is not rigid but dynamically adjusts based on real-time feedback, allowing the system to collect reliable perception data efficiently by focusing computational resources on the most informative measurements.
Solution Approach 2:
The system uses feedback mechanisms where perception data from initial scans informs subsequent scanning operations. The robot continuously evaluates the quality and completeness of collected data, adjusting its scanning strategy in real-time to ensure reliable object reference generation while maintaining high productivity through intelligent resource allocation.
Data Source
AI summary
A method in accordance with a particular embodiment of the present technology includes receiving, at data-processing hardware operably associated with a mobile robot, perception data corresponding to an object in an environment of the mobile robot. The method further includes generating, by the data-processing hardware and based at least partially on the perception data, a feature reference corresponding to a feature of the object. The method also includes generating, by the data-processing hardware, candidate object references based at least partially on different respective alignments between the feature reference and a model of the object. The method also includes selecting, by the data-processing hardware, one of the candidate object references based at least partially on a result of evaluating the candidate object references for accuracy. Finally, the method includes controlling, by the data-processing hardware, the mobile robot based at least partially on the selected candidate object reference.