Adaptive Sensor Fusion for Mobile Object 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 sensors like GPS may fail or provide inaccurate data.
Innovation Solution
A self-position estimation apparatus that utilizes a combination of odometry, GPS, and multiple scanners (2D and 3D LiDAR) to select and combine information from various sensors based on environmental determinations, ensuring accurate positioning even in environments where individual sensors may fail, such as indoors or areas with high natural or artificial object presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single sensor (e.g., GPS) is used for self-position estimation, then the device complexity is reduced, but the reliability of position estimation deteriorates in environments where the sensor fails or provides inaccurate data
Solution Approach 1:
The patent combines multiple sensors (GPS receiver, odometry device, and scanner) into an integrated self-position estimation system. The position estimation unit processes data from all these sensors together, allowing the system to maintain reliable position estimation even when individual sensors fail or provide inaccurate data in certain environments.
Solution Approach 2:
The patent creates a multi-functional sensor system where each sensor serves multiple purposes. The scanner not only detects obstacles but also provides environmental information for position estimation. The odometry device provides both movement data and supplementary position information. This universal approach ensures that the system can estimate position reliably across diverse environments using multiple functional pathways.
2Reliability
If multiple sensors are used for self-position estimation, then the reliability improves, but the device complexity increases
Solution Approach 1:
The position estimation unit acts as an intermediary that processes and integrates data from multiple sensors (GPS receiver, odometry device, scanner). This intermediary component coordinates the information from different sensors, resolving the complexity by providing a unified processing mechanism that manages multiple data sources without requiring complex external integration systems.
3Measurement precision
If GPS is used for position estimation, then the device complexity is reduced, but the measurement precision deteriorates in indoor environments or areas with high object presence
Solution Approach 1:
The patent applies local quality by selecting different sensors or sensor combinations based on the local environment. The position estimation unit determines whether to prioritize GPS data, odometry data, or scanner data depending on the specific environmental conditions (indoor vs. outdoor, presence of obstacles). This localized adaptation ensures high measurement precision in each specific environment without requiring a complex fixed configuration.
Solution Approach 2:
The patent implements a dynamic sensor selection and data fusion approach where the position estimation unit continuously adapts which sensors to use and how to weight their data based on current environmental conditions. This dynamic adjustment allows the system to maintain high measurement precision across varying environments (indoor, outdoor, obstructed areas) without requiring a statically complex sensor configuration for every possible scenario.
4Reliability
If odometry is used for position estimation, then the device complexity is reduced, but the reliability deteriorates when wheel slip or rotation errors occur
Solution Approach 1:
The patent implements beforehand cushioning by having the position estimation unit prepared to switch to alternative sensors (GPS receiver or scanner) when odometry data becomes unreliable. The system anticipates potential wheel slip or rotation errors and has pre-configured fallback mechanisms in place, allowing it to maintain reliable position estimation without requiring complex real-time error correction systems for odometry.
Data Source
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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.