Multi-Sensor Self-Localization Selection for Changing Environments
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Solution Overview
Problem
Existing self-localization technologies for mobile objects, such as vehicles, fail to maintain accuracy in estimating positions due to changes in surrounding environments, such as time zones and weather conditions, leading to reduced collision-avoidance assistance.
Innovation Solution
A self-localization estimation apparatus that utilizes a plurality of sensors, including GNSS, cameras, radar, sonar, and LiDAR, to perform multiple self-position estimation tasks with varying accuracies, and an output determiner that selects the most accurate position based on analysis of estimation results and sensor characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used for self-localization estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the self-localization estimation into multiple independent estimation tasks, each handled by a specific sensor or sensor combination. Each estimation task is processed by a dedicated estimation unit that operates independently, allowing the system to manage complexity through modular segmentation while maintaining high measurement precision through multiple specialized estimators.
Solution Approach 2:
The output self-position determiner serves as a universal component that integrates and selects from multiple estimation results regardless of their sources. This multi-functional determiner handles various sensor types (GNSS, cameras, radar, sonar, LiDAR) and their different estimation accuracies, providing a unified interface that simplifies the overall system architecture while maintaining high precision.
2Adaptability or versatility
If estimation accuracy is reduced to handle environmental changes, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements dynamic adjustment of estimation accuracy levels based on current environmental conditions. The output self-position determiner selects appropriate estimation tasks and accuracy levels dynamically, switching between high-precision estimators when conditions permit and more robust but less precise estimators when environmental factors like poor weather or nighttime reduce sensor performance, thus maintaining adaptability while preserving measurement precision when possible.
Solution Approach 2:
The system changes the parameter of estimation accuracy levels based on environmental conditions. By adjusting which estimation tasks are executed and their respective accuracy requirements according to weather, time of day, and other environmental factors, the system adapts to varying conditions while maintaining the highest possible measurement precision under each specific situation.
Data Source
AI summary
In a self-localization estimation apparatus for a mobile object equipped with sensors, a self-position estimating unit performs self-position estimation tasks corresponding to the respective sensors with respective estimation accuracies. Each of the estimation accuracies depends on a measurement characteristic of the corresponding one of the sensors. An output self-position determiner determines, based on analysis of estimation results and the estimation accuracies of the respective self-position estimation tasks, a self-position of the mobile object estimated by a selected one of the self-position estimation tasks as an output self-localization position of the mobile object.