Multi-camera Distance Measurement for SLAM Accuracy
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Solution Overview
Problem
Current technologies for self-position estimation and environment mapping, such as SLAM, face challenges in accurately measuring distances between target objects using portable devices like smartphones, particularly in applications like measuring passageway widths.
Innovation Solution
An information processing apparatus and method that utilize multiple imaging devices, including digital cameras or ToF sensors, to calculate distance information based on actual distances, position, and attitude information, allowing for accurate measurement of distances between target objects by modeling the environment and integrating data from motion sensors and distance measurement sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SLAM technology is used for self-position estimation and environment mapping, then the ability to model the real world is improved, but the accuracy of measuring distances between target objects deteriorates
Solution Approach 1:
The system divides the measurement task into multiple independent components: first imaging device captures first target object, second imaging device captures second target object, motion sensors track device movement, and distance measurement sensors provide reference data. Each component operates independently and their results are integrated to achieve accurate distance measurement between target objects.
Solution Approach 2:
The system merges data from multiple sources including images from first and second imaging devices, position and attitude information from motion sensors, and distance references from distance measurement sensors. By combining these diverse data sources through image processing and integration, the system achieves both real world modeling capability and accurate distance measurement.
2Measurement precision
If multiple imaging devices are used to improve measurement accuracy, then the precision of distance measurement is improved, but the complexity of the device increases
Solution Approach 1:
The imaging devices are designed to serve multiple functions: capturing images of target objects for distance measurement, providing visual data for environment mapping, and enabling self-position estimation. This multi-functionality reduces the need for separate dedicated components for each function, thereby managing device complexity while maintaining measurement precision.
Solution Approach 2:
The system introduces an image processing unit as an intermediary that integrates data from multiple imaging devices and sensors. This intermediary component coordinates the information flow between diverse sources (imaging devices, motion sensors, distance measurement sensors) and produces unified distance measurement results, simplifying the overall system architecture.
3Measurement precision
If position and attitude information from motion sensors is integrated, then the accuracy of self-position estimation is improved, but the complexity of data processing increases
Solution Approach 1:
The system performs preliminary processing of position and attitude information from motion sensors before integrating it with imaging data. By pre-processing and organizing sensor data in advance, the system reduces the computational burden during final integration, thereby improving self-position estimation accuracy while managing data processing complexity.
Solution Approach 2:
The system implements feedback mechanisms where the results of distance measurement and self-position estimation are continuously refined using ongoing data from imaging devices and motion sensors. This feedback loop allows the system to correct errors and improve accuracy over time while maintaining manageable processing complexity through iterative optimization.
Data Source
AI summary
An information processing apparatus according to an embodiment of the present technology includes an output control unit. The output control unit outputs distance information based on an actual distance between a first target object imaged by a first imaging device and a second target object imaged by a second imaging device on the basis of scale information regarding the actual distance, first position information regarding a position and an attitude of the first imaging device, and second position information regarding a position and an attitude of the second imaging device. Accordingly, a distance between two target objects can be easily and accurately measured.