Adaptive Sensor Selection for Mobile Self-Position Estimation
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Solution Overview
Problem
Existing self-position estimation systems for mobile objects face challenges in accurately estimating their position regardless of the environment, particularly in environments where certain sensors may not function effectively, leading to inaccurate position estimation.
Innovation Solution
A self-position estimation apparatus that utilizes multiple sensors (such as odometry, GPS, and LiDAR scanners) to detect different types of information, with an environment determiner selecting the appropriate sensor data for estimation based on the environment, ensuring accurate position estimation even when some sensors are unreliable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple types of sensors are used to detect information regarding moving status, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects and switches between different sensor types based on environmental conditions. The environment determiner continuously assesses the current environment and the selector adjusts which sensors are active, making the sensor system adaptive rather than static. This resolves the contradiction by activating only the necessary sensors for current conditions, improving precision when needed while reducing complexity when not.
Solution Approach 2:
The system changes the operational parameters of the sensor system by switching between different sensor types (odometry, GPS, LiDAR) depending on environmental parameters. When the environment is determined to be suitable for GPS, it switches to GPS; when odometry is more reliable, it switches to odometry. This parameter-based selection optimizes measurement precision while managing device complexity through conditional activation.
2Reliability
If sensor data is selectively used based on environment determination, then reliability is improved, but device complexity increases
Solution Approach 1:
The environment determiner performs preliminary assessment of environmental conditions before selecting sensor data for position estimation. By evaluating the environment in advance and determining which sensors will be most reliable, the system prepares the optimal sensor configuration beforehand. This preliminary action ensures reliability by preventing use of unreliable sensors while the structured environment assessment process manages complexity through systematic evaluation.
Solution Approach 2:
The system implements feedback through the environment determiner that continuously monitors environmental conditions and adjusts sensor selection accordingly. The selector receives feedback from the environment determiner about current conditions and adjusts which sensors are active. This closed-loop feedback mechanism improves reliability by adapting to environmental changes while managing complexity through automated decision-making based on environmental parameters.
Data Source
AI summary
Provided is a self-position estimation apparatus capable of appropriately estimating a self-position of a mobile object regardless of an environment around the mobile object. The self-position estimation apparatus is for estimating a self-position of a mobile object, and includes: N types of sensors (where N is a natural number equal to or greater than two) that detect information with different contents from each other regarding a moving status of the mobile object; an environment determiner that determines an environment around the mobile object; a selector that selects information detected by one or more but less than the N types of sensors based on a determination result of the environment determiner; and an estimator that estimates the self-position of the mobile object based on the information selected by the selector.