Autonomous Vehicle Sensor Fusion Positioning With CPU-GPU Task Split
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Solution Overview
Problem
Existing methods for visual/LiDAR odometry in autonomous vehicles consume a significant amount of CPU resources, making them inefficient in terms of computing power and resource utilization.
Innovation Solution
A method is developed to process data for autonomous vehicles by acquiring sensor data including inertial measurement, LiDAR, and visual image data, and determining constraint factors for each type of data. These constraint factors are then used to process the sensor data, obtaining positioning data for the vehicle while optimizing CPU resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing visual/LiDAR odometry methods are used for autonomous vehicle positioning, then positioning functionality is achieved, but CPU computing resources are consumed excessively
Solution Approach 1:
The patent segments the computing workload by separating CPU-based operations from GPU-based operations. The CPU performs only essential tasks such as data acquisition coordination and final result integration, while the GPU handles computationally intensive tasks including constraint factor determination, sensor data processing, and optimization calculations. This segmentation resolves the contradiction by maintaining positioning precision through distributed computation while dramatically reducing CPU resource consumption.
2Productivity
If more CPU resources are allocated for data processing, then processing speed improves, but overall system efficiency decreases due to suboptimal resource utilization
Solution Approach 1:
The patent introduces a computing task distribution mechanism as an intermediary between the data processing requirements and the hardware resources. This intermediary intelligently allocates computational tasks based on their characteristics: parallelizable tasks are assigned to the GPU, while sequential or control-oriented tasks remain on the CPU. This resolves the contradiction by optimizing productivity through appropriate task placement while preventing computing power waste through efficient resource matching.
Data Source
AI summary
A method of processing data for an autonomous vehicle, an electronic device, a storage medium, and an autonomous vehicle are provided. The method includes: acquiring sensor data for the autonomous vehicle, wherein the sensor data includes inertial measurement data, LiDAR data, and visual image data; determining a first constraint factor for the inertial measurement data according to the inertial measurement data and the visual image data; determining a second constraint factor for the LiDAR data according to the inertial measurement data and the LiDAR data; determining a third constraint factor for the visual image data according to the inertial measurement data, the visual image data and the LiDAR data; and processing the sensor data based on the first constraint factor, the second constraint factor and the third constraint factor, so as to obtain positioning data for positioning the autonomous vehicle.