Mobile Position Estimation Using Landmark Motion Consistency
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Solution Overview
Problem
Existing position estimation methods for mobile entities, such as robots and automobiles, face decreased recognition accuracy due to unaccounted vibrations and calibration errors, which affect the accuracy of landmark recognition and subsequent position estimation.
Innovation Solution
A position estimation device equipped with imaging devices and an information processing unit that calculates the movement amount of detection points and adjusts the accuracy of recognition to improve position estimation, even during errors in calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration is performed using images before and after vehicle attitude change, then camera parameters can be corrected, but vibration effects and calibration errors are not considered, resulting in decreased landmark recognition accuracy
Solution Approach 1:
The system performs preliminary calibration by capturing multiple images of a calibration pattern at different vehicle attitudes before actual operation. This preliminary calibration data is stored and used to compensate for vibration and attitude changes during runtime, improving both landmark recognition and position estimation accuracy
Solution Approach 2:
The system continuously monitors the detected attitude of the vehicle using IMU data and compares it with the calibrated reference attitudes. Based on this feedback, the system dynamically selects or adjusts the appropriate calibration parameters to compensate for current vibration and attitude effects, maintaining high accuracy in landmark recognition and position estimation
2Manufacturing precision
If camera calibration is performed at the factory, then initial positioning accuracy can be ensured, but attitude changes due to passengers and luggage require recalibration after shipment
Solution Approach 1:
Multiple calibration datasets are captured during manufacturing under different simulated loading conditions (different passenger configurations, luggage arrangements). These pre-captured calibration datasets cover a range of possible operating conditions, allowing the system to adapt to different loading scenarios without requiring physical recalibration
Solution Approach 2:
The system stores multiple sets of camera calibration parameters corresponding to different vehicle attitudes and loading conditions. Based on the detected current vehicle state (from IMU and other sensors), the system selects or interpolates the most appropriate calibration parameters, enabling adaptation to varying loading conditions while maintaining manufacturing precision
3Ease of operation
If SLAM method is used for position estimation, then position can be estimated without GPS or landmarks, but error accumulates over time requiring frequent position correction
Solution Approach 1:
The system merges SLAM-based relative position estimation with landmark-based absolute position correction. The SLAM algorithm provides continuous navigation capability while landmark detection periodically corrects accumulated errors by providing absolute position references, achieving both autonomous navigation and high position accuracy
Data Source
AI summary
Improvement in the accuracy of estimating the position of a mobile entity even while traveling or if there is an error in the calibration performed utilizing: a mobile entity; an imaging device provided in the mobile entity; and an information processing device for determining a first movement amount by which a detection point that is the same object has moved on the basis of a first image and a second image acquired by the imaging device and a second movement amount by which the mobile entity has moved while the first image and the second image were acquired, determining the accuracy of recognizing the detection point acquired by the imaging device on the basis of the first movement amount and the second movement amount, and estimating the position of the mobile entity on the basis of the accuracy of recognition and position information that pertains to the detection point.