Sensor-Based Pose Measurement Using Object Variation Filtering
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Solution Overview
Problem
Existing autonomous driving robots face challenges in accurately determining position and orientation due to objects with changing features, leading to reduced measurement accuracy.
Innovation Solution
An information processing apparatus that acquires object variation characteristics and proximity object factor characteristics to control position and orientation measurement, excluding features from objects with high variation degrees to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If features from all objects are used for position and orientation measurement, then measurement data coverage is improved, but measurement accuracy deteriorates due to objects with changing features
Solution Approach 1:
The system segments objects into different categories (proximity objects and non-proximity objects) based on their spatial relationship with the measurement target. This segmentation allows differential processing where proximity objects undergo detailed variation factor analysis while non-proximity objects are processed more simply, resolving the contradiction between comprehensive measurement and manageable complexity
Solution Approach 2:
The system applies different processing qualities to different objects: proximity objects receive intensive analysis of variation factors (movement, shape change, appearance change) while non-proximity objects receive standard processing. This local differentiation optimizes measurement accuracy for critical objects without uniformly increasing system complexity
Solution Approach 3:
The system performs preliminary analysis of object variation factors before conducting position and orientation measurement. By pre-identifying objects with high variation likelihood (proximity objects) and pre-classifying them, the system prepares measurement strategies in advance, avoiding complex real-time analysis during the actual measurement process
2Measurement precision
If features from objects with high variation are excluded, then measurement accuracy is improved, but loss of useful measurement information increases
Solution Approach 1:
The system changes the parameter of object selection from static (all objects or simple exclusion) to dynamic (based on calculated variation factors). By computing variation factors and using them as selection parameters, the system adaptively determines which objects to include or exclude, maximizing measurement accuracy while minimizing information loss through intelligent rather than arbitrary exclusion
Solution Approach 2:
The system uses feedback from the variation factor analysis to control the measurement process. The calculated variation factors provide feedback that informs the selection of objects for measurement, creating a closed-loop system that continuously optimizes which objects contribute to the measurement, thereby reducing information loss while maintaining accuracy
Data Source
AI summary
An information processing apparatus configured to perform position and orientation measurement by using sensor data from a sensor is provided, comprising a first characteristic acquisition unit configured to acquire object variation characteristics that indicate characteristics that influence the accuracy of the position and orientation measurement for each object within the measurement range of the sensor, a proximity object determination unit configured to determine a proximity object that interferes with an object from which the object variation characteristics have been acquired, a second characteristic acquisition unit configured to acquire object variation factor characteristics that indicate characteristics that influence the accuracy of the position and orientation measurement by interfering with other objects with respect to each of the proximity objects, and a position and orientation measurement control unit configured to control the position and orientation measurement processing so as to restrict the use of information in which variation is estimated among the information of objects within the measurement range of the sensor, based on the object variation characteristics and the object variation factor characteristics.