Unified Sensor Fusion via Optimization for Unknown Parameters
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Solution Overview
Problem
Existing sensor fusion methods are limited in their ability to handle various types of systems and parameters, requiring different solution methods for specific settings, which can be cumbersome and inefficient for systems with unknown geometric and sensor parameters.
Innovation Solution
A unified method that maps the computation of unknown parameters to an optimization problem, allowing for the determination of both sensor and geometric parameters by adjusting the optimization problem formulation based on the system configuration, enabling real-time switching between different types of systems and parameters without the need for reprogramming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple specific sensor fusion methods are provided for different settings, then measurement precision is improved for specific applications, but device complexity increases and adaptability decreases
Solution Approach 1:
The patent implements a universal sensor fusion method that can handle multiple types of sensors (cameras, LIDAR, radar, IMU, etc.) and multiple application scenarios (3D reconstruction, SLAM, calibration, pose estimation) through a single unified optimization framework. The configuration data structure allows flexible specification of sensor types, target objects, and parameter types, enabling the same core method to adapt to different settings without requiring separate specialized algorithms for each application.
2Measurement precision
If multiple specific sensor fusion methods are provided for different settings, then measurement precision is improved for specific applications, but device complexity increases
Solution Approach 1:
The patent merges multiple specialized sensor fusion methods into a single unified optimization-based approach. Instead of maintaining separate algorithms for 3D reconstruction, SLAM, calibration, and pose estimation, the invention combines them all into one framework that uses configuration data to specify the problem type and solves it through a general optimization process, thereby reducing overall system complexity.
3Adaptability or versatility
If a unified method is implemented for arbitrary systems, then adaptability is improved, but measurement precision may worsen due to generalization
Solution Approach 1:
The patent applies local quality by allowing the configuration data to specify problem-specific parameters and constraints for each particular application. While the core optimization framework is universal, the configuration can be tailored to each specific scenario (e.g., specifying camera intrinsics for 3D reconstruction, or IMU parameters for SLAM), ensuring that each application receives the appropriate level of specialization for optimal measurement precision.
Data Source
AI summary
A method of sensor fusion for systems including at least one sensor and at least one target object is provided. The method includes receiving configuration data at a processing device. The configuration data includes a description of a first system including one or more sensors and one or more target objects. The configuration data includes an indication that one or more geometric parameters and/or one or more sensor parameters of the first system are unknown. The method includes receiving an instruction at the processing device that the received configuration data is to be adjusted into adjusted configuration data. The adjusted configuration data includes a description of a second system including one or more sensors and one or more target objects, wherein the second system is different from the first system. The adjusted configuration data includes an indication that one or more geometric parameters and/or one or more sensor parameters of the second system are unknown. The method includes receiving, for each sensor of the second system, measurement data resulting from a plurality of measurements performed by the sensor. The method includes determining an optimization problem using the processing device, wherein each unknown geometric parameter and each unknown sensor parameter of the second system are associated with one or more variables of the optimization problem. The method includes determining a value of each unknown geometric parameter and a value of each unknown sensor parameter of the second system by solving the optimization problem using the processing device.