Mobile Device Positioning via Reference Map Sensor Selection
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Solution Overview
Problem
Existing methods for locating a mobile device, such as SLAM and Monte Carlo Localization, face challenges in accurately determining position due to sensor errors and varying detection directions, leading to incorrect measurements, especially in environments with diverse landmarks and changing conditions.
Innovation Solution
A system that utilizes a reference map to recommend the most suitable locating method for each position based on sensor perspectives, ranges, and landmark distributions, allowing for adaptive selection and deactivation of sensors to minimize errors and energy consumption, while using a combination of sensors like cameras and ultrasound for predictive path planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used for locating the mobile device, then the position estimation accuracy is improved, but the energy consumption increases
Solution Approach 1:
The system dynamically adapts the locating method based on the device's current position and movement state. The control apparatus selects between different locating methods (e.g., GPS-only, sensor fusion) depending on whether the device is moving or stationary, and which positions have been previously detected, thereby optimizing energy consumption while maintaining positioning accuracy.
Solution Approach 2:
The system changes operational parameters by switching between different locating methods with different energy characteristics. The control apparatus adjusts the activation state of various sensors and locating algorithms based on position history and current context, effectively managing the trade-off between accuracy and energy usage.
2Reliability
If sensor data merging is performed for all positions, then the locating robustness is improved, but the computational complexity increases
Solution Approach 1:
The system segments the operational space into different position categories (previously detected positions vs. new positions, moving state vs. stationary state). For each segment, a specific locating method is selected, avoiding the need to apply complex sensor fusion algorithms universally. This reduces computational complexity while maintaining robustness where needed.
Solution Approach 2:
The system applies sensor data merging selectively rather than universally. Sensor fusion is performed only for positions that require it (e.g., new positions or positions where GPS accuracy is insufficient), while previously detected positions use simpler locating methods, reducing overall computational burden.
3Device complexity
If the same locating method is used in every situation, then the system simplicity is maintained, but the position estimation accuracy deteriorates
Solution Approach 1:
The system transitions from a static, one-size-fits-all locating approach to a dynamic, context-aware system. The control apparatus automatically selects the appropriate locating method based on real-time conditions such as device movement state, position history, and environmental context, thereby improving accuracy without requiring manual intervention.
Solution Approach 2:
The system uses a reference map that stores previously detected positions and their associated locating methods. When the device returns to a previously detected position, the system copies the stored location data, avoiding redundant computations and maintaining accuracy while simplifying the current operation.
4Adaptability or versatility
If all sensors are activated continuously, then the detection coverage is improved, but the energy consumption increases
Solution Approach 1:
The system activates sensors periodically or on-demand rather than continuously. Sensors are activated only when the device enters a new position or when the current locating method requires additional sensor data, reducing energy consumption while maintaining comprehensive detection coverage when needed.
Solution Approach 2:
The control apparatus autonomously manages sensor activation based on the device's position and movement state. The system self-adjusts which sensors are active without external intervention, activating only the necessary sensors for the current locating task, thereby reducing energy consumption while maintaining detection versatility.
Data Source
AI summary
The invention relates to a method for locating a mobile device in a surrounding area, wherein the device has multiple sensors for detecting the area surrounding the device using different locating methods, wherein a reference map is provided for the surrounding area, said reference map comprising multiple positions within the surrounding area, wherein at least one locating method which is to be carried out using at least one sensor in order to detect the surrounding area is recommended for at least one position within the surrounding area, wherein the at least one locating method recommended according to the reference map and to be carried out using at least one sensor is used for a current position of the mobile device in order to locate the device.