Multi-Sensor Fusion Positioning for GPS-Denied Navigation
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Solution Overview
Problem
Current positioning technologies for autonomous vehicles and UAVs rely heavily on GPS and LiDAR, which can fail in scenarios with blocked satellite signals or degraded LiDAR observations, leading to inaccurate positioning in complex environments.
Innovation Solution
A multi-sensor fusion method that synchronizes and processes data from various sensors, including IMU, LiDAR, and wheel speedometers, within a sliding window to determine the current pose state of a movable object, enabling accurate positioning even without GPS signals or robust LiDAR data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and LiDAR are used for positioning, then positioning accuracy is improved, but positioning reliability deteriorates in scenarios with blocked satellite signals or degraded LiDAR observations
Solution Approach 1:
The patent combines multiple sensors (IMU, wheel speedometer, LiDAR, camera) into a fusion positioning system. The sensor fusion module integrates data from all sensors to compensate for individual sensor failures, ensuring reliable positioning even when GPS is blocked or LiDAR observations are degraded.
Solution Approach 2:
The patent dynamically adjusts the weighting and selection of sensor data based on environmental conditions. When GPS is unavailable or LiDAR is degraded, the system changes its reliance on alternative sensors (IMU, wheel speedometer, camera), adapting the parameter configuration to maintain positioning accuracy and reliability.
2Reliability
If multiple sensors are integrated for positioning, then positioning reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the positioning system into modular components: sensor data acquisition modules for each sensor type, a time-synchronization module, a sensor fusion module, and a positioning module. This segmentation allows each component to be developed, tested, and maintained independently, reducing overall system complexity.
Solution Approach 2:
The patent introduces intermediary processing layers including a time-synchronization module that aligns data from multiple sensors with different sampling rates, and a sensor fusion module that mediates between raw sensor data and final positioning results. These intermediaries simplify the integration process and reduce direct complexity.
3Speed
If real-time sensor data processing is performed, then positioning speed is improved, but computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing sensor data (calibration, noise filtering, feature extraction) before the main positioning computation. The time-synchronization module pre-aligns temporal data, and the sensor fusion module pre-processes data correlations, reducing the computational burden during real-time positioning operations.
Solution Approach 2:
The patent applies partial action by selectively processing only the necessary sensor data and computational operations required for the current positioning task. The system processes sensor data in batches and uses efficient algorithms that compute only essential positioning parameters, reducing overall computational resource consumption while maintaining real-time performance.
Data Source
AI summary
A positioning method and device based on multi-sensor fusion are provided and relates to object positioning field. The method includes obtaining sensor data collected by various sensors on a movable object in real time; temporally and spatially synchronizing sensor data collected by the sensors to form various temporally and spatially synchronized sensor data; performing data preprocessing and correlation on the temporally and spatially synchronized sensor data to form to-be-jointly-optimized sensor data; obtaining state information at each time point before a current time point in a preset sliding window; and determining a current pose state of the movable object by performing a joint optimization according to the to-be-jointly-optimized sensor data and the state information at each time point before the current time point in the sliding window. The movable object can be accurately positioned in scenarios where GPS signals are lost, jumping exists, or LiDAR observation is degraded seriously.