Probabilistic Consensus for Multi-Agent Map Fusion
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Solution Overview
Problem
Multi-agent mapping systems face challenges in effectively combining sensor data from diverse sources, particularly in detecting outliers and low-fidelity information, which can compromise the accuracy of fusion results due to varying sensor quality from different autonomous vehicles using different sensors.
Innovation Solution
A system that updates a probability density function by leveraging multi-agent localization and sensor analysis to rank sensor quality, using a fusion engine to combine sensor data from multiple vehicles, and triggering maintenance alerts for low-quality sensors, thereby improving sensor models and map accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data from multiple autonomous vehicles is combined to update maps, then map coverage and productivity are improved, but the reliability of fusion results deteriorates due to varying sensor quality and presence of outliers
Solution Approach 1:
The patent replaces traditional mechanical filtering methods with probabilistic consensus algorithms that use probability density functions to model sensor reliability. This substitution allows for more sophisticated handling of sensor quality variations and outlier detection in the fusion process.
Solution Approach 2:
The system implements feedback mechanisms where sensor performance is continuously evaluated and used to adjust fusion weights dynamically. The probabilistic consensus algorithm uses feedback about sensor reliability to optimize future fusion operations, improving both accuracy and efficiency.
2Device complexity
If all sensor data is treated equally in fusion, then device complexity is reduced, but measurement precision deteriorates due to inclusion of low-fidelity information
Solution Approach 1:
The patent applies local quality by assigning different weights and treatment levels to different sensor data based on their individual reliability characteristics. Each sensor's probability density function is used to determine the quality level, allowing precise handling of each data source according to its specific properties rather than uniform treatment.
Solution Approach 2:
The system changes the parameters of the fusion process dynamically by adjusting the probability density functions and fusion weights based on real-time sensor performance evaluation. This allows the system to adapt to varying sensor qualities without increasing overall algorithmic complexity.
3Measurement precision
If sensor quality ranking is implemented to improve fusion accuracy, then measurement precision is improved, but device complexity increases due to additional sensor analysis and diagnostics
Solution Approach 1:
The patent implements self-service by enabling sensors to self-evaluate their own performance and generate their own probability density functions. Sensors autonomously provide quality metrics and reliability information, reducing the need for complex external analysis systems while maintaining high measurement precision.
Data Source
AI summary
Various systems and methods for updating a current probability density function. The PDF includes a list of objects within a location. First information is received from a first remote device at a first position within the location. The first information includes a vehicle identification, a first PDF of the objects. Second information is received from a second remote device at a second position within the location. The second information includes a vehicle identification, a second PDF of the objects. A first rank for the first PDF is determined based on locations of the objects in the first PDF compared to locations of the objects in the current PDF. A second rank for the second PDF is determined. The first PDF and the second PDF are combined using the first rank and the second rank into a combined PDF. The current PDF based on the combined PDF.