Pose Graph Fusion of Position Data for Real-Time Localization
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Solution Overview
Problem
Existing positioning techniques, particularly those using sensor fusion based on Kalman filters, face limitations in flexibility and accuracy when handling a wide variety of positioning systems and are not suitable for real-time applications, especially in scenarios where positioning systems do not function reliably or transition between different data sources.
Innovation Solution
A pose graph with chain geometry is used to efficiently optimize the fusion of odometry and absolute position data from multiple sources, allowing for flexible sensor fusion and accurate position estimation by employing a block three-diagonal system matrix for computationally efficient inversion and reduced optimization time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a pose graph with chain geometry is used for sensor fusion, then computational efficiency and real-time performance are improved, but the ability to handle complex positioning scenarios with multiple data sources may be limited
Solution Approach 1:
The pose graph is segmented into a chain geometry structure where the graph is divided into sequential nodes and edges representing position data at different time points. This segmentation enables efficient computational processing while maintaining the ability to represent complex positioning scenarios through the sequential arrangement of nodes that can capture multiple data sources in a time-ordered sequence.
2Measurement precision
If traditional sensor fusion methods are used to handle a wide variety of positioning systems, then flexibility and accuracy are improved, but computational complexity and time consumption increase
Solution Approach 1:
The invention changes the parameter representation by organizing position data into a pose graph structure with nodes representing positions at different time points and edges representing transitions. This parameter transformation enables the system to handle multiple positioning systems with different accuracies and time stamps efficiently, reducing computational complexity while maintaining position estimation accuracy through the structured graph representation.
3Measurement precision
If comprehensive sensor fusion of multiple positioning systems is performed, then position estimation accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The pose graph structure performs preliminary organization of position data from multiple positioning systems before actual fusion computation. By pre-structuring the data into nodes and edges with proper time ordering and relationships, the system reduces the computational burden during the actual fusion process, enabling faster processing while maintaining comprehensive use of multiple data sources for accurate position estimation.
Data Source
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AI summary
Absolute position data (605) of a machine are determined for respective multiple times (t.1, t.3, t.5, t.8, t.11), and odometry position data of the machine are alsodetermined. A pose graph (661) is generated, wherein edges (672) of the pose graph (661) correspond to the odometry position data, and nodes (671) of the pose graph (661) correspond to the absolute position data (605). The pose graph (661) is optimised to obtain an estimated position. Optionally, the odometry can also be estimated. A driver assistance functionality of the machine, for example a motor vehicle, can be controlled optionally on the basis of the estimated position. For example, the driver assistance functionality can relate to autonomous driving.