Vehicle Pose Sub-Map Switching for Accurate Localization
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Solution Overview
Problem
Existing autonomous vehicle systems face challenges in accurately determining vehicle pose when transitioning between different sub-maps, leading to inefficiencies and potential errors in navigation.
Innovation Solution
A pose system that includes a pose state estimator and multiple localizers, which utilize remote sensors and motion sensors to generate vehicle poses and switch between sub-maps based on error thresholds and covariance indicators, ensuring accurate pose estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses multiple localizers and sub-maps to improve pose estimation accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The map is divided into multiple sub-maps, each covering a specific geographic area. The system selects and activates only the sub-map relevant to the current vehicle location, reducing the computational burden of processing entire map data while maintaining high pose estimation accuracy through localized feature matching.
Solution Approach 2:
The system dynamically switches between different sub-maps based on the vehicle's current location and motion state. This dynamic adaptation allows the system to maintain high measurement precision across different geographic areas while avoiding the complexity of processing all map data simultaneously.
2Adaptability or versatility
If the system frequently switches between sub-maps to track vehicle movement, then adaptability is improved, but loss of time increases due to switching overhead
Solution Approach 1:
The system pre-processes and organizes map data into multiple sub-maps during system initialization or offline preparation. This preliminary action enables rapid switching between sub-maps during operation without requiring complex real-time processing, thus improving adaptability while minimizing time loss.
Solution Approach 2:
The system continuously monitors vehicle position, motion state, and pose estimation quality to determine when sub-map switching is necessary. This feedback mechanism prevents unnecessary switching, reducing time loss while maintaining the adaptability to switch when truly needed for accurate tracking.
3Reliability
If the system processes data from multiple sensors and localizers to reduce errors, then reliability is improved, but use of energy increases
Solution Approach 1:
The pose state estimator serves multiple functions: it processes data from multiple localizers, performs pose estimation, manages sub-map switching, and filters sensor data. This multi-functionality allows the system to achieve high reliability through comprehensive data processing while avoiding the energy overhead of separate dedicated components for each function.
Solution Approach 2:
The system adjusts processing parameters dynamically based on vehicle motion state and environmental conditions. When the vehicle is stationary or moving slowly, the system reduces processing frequency and complexity. When moving quickly or in challenging environments, the system increases processing intensity. This parameter adaptation maintains reliability while optimizing energy consumption.
Data Source
AI summary
Various examples are directed to systems and methods for locating a vehicle. A pose state estimator may access a previous position for the vehicle at a first time, wherein the previous position is on a first sub-map of a plurality of sub-maps. The pose state estimator may receive from a first localizer a first position estimate for the vehicle at a second time after the first time. The first position estimate may be on a second sub-map of the plurality of sub-maps. The pose state estimator may send to a second localizer a sub-map change message indicating the second sub-map.