Autonomous Device Position Estimation Error Correction
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Solution Overview
Problem
Autonomous movement devices face challenges in accurately estimating their local position and creating maps in real-time due to errors from wheel friction, slip, and other environmental factors, leading to decreased precision over time.
Innovation Solution
The autonomous movement device employs a combination of a monocular camera for image processing, odometry for position measurement, and sensors like angular speed and magnetism to improve direction precision, along with a controller that executes SLAM algorithms to estimate and correct the local device position, and moves to positions where estimation is uniquely defined.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If SLAM algorithm is used for real-time position estimation and map creation, then autonomous navigation capability is improved, but measurement precision deteriorates over time due to cumulative errors from wheel friction and slip
Solution Approach 1:
The system continuously compares the estimated position from SLAM with actual position measurements from sensors (odometry, angular speed sensor, magnetism sensor) and uses this feedback to correct cumulative errors. The controller adjusts the position estimation based on feedback from multiple sources to maintain accuracy over time.
Solution Approach 2:
The system changes the parameters used for position estimation by switching between different sensor inputs and estimation methods. When SLAM alone becomes unreliable, the system incorporates odometry data, angular speed measurements, and magnetism readings to recalculate position, effectively changing the estimation parameters to maintain precision.
2Measurement precision
If multiple sensors and correction mechanisms are added to improve position estimation precision, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The controller serves multiple functions: it executes SLAM algorithms, processes odometry data, reads angular speed sensor measurements, interprets magnetism sensor data, and performs position correction. By making the controller multi-functional, the system avoids adding separate dedicated hardware for each function, thus managing complexity while maintaining precision.
Solution Approach 2:
The system merges multiple position estimation approaches (SLAM, odometry, sensor-based measurement) into a unified correction mechanism. Instead of operating as separate independent systems, these methods are combined in the controller to work together, reducing overall system complexity while improving measurement precision through their integration.
Data Source
AI summary
To quickly recover from a status in which a local device position estimation is unavailable. A movement processor of an autonomous movement device executes a drive process for a driver so as to move autonomously. A position estimator estimates the local device position. An estimation determiner determines whether or not the estimation result of the local device position by the position estimator is uniquely defined. A movement process controller controls the movement processor so as to move to a position where the estimation result of the local device position by the position estimator is uniquely defined when the estimation determiner determines that the estimation result is not defined uniquely.