Map Scanning System Using Lidar Matching for Positioning Reliability
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Solution Overview
Problem
Current location positioning technologies for autonomous vehicles, such as those using sensors, are unreliable in special environments like tunnels or heavy rain, leading to inaccurate or unusable data.
Innovation Solution
A map scanning system utilizing a lidar sensor and image sensor to generate point cloud and image data, which is compared with a basic map to determine the vehicle's location and update the map when necessary, allowing for accurate location determination without GPS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensors are used for location positioning, then the positioning function can be implemented under normal conditions, but the reliability deteriorates in special environments such as tunnels or heavy rain
Solution Approach 1:
The patent introduces a basic map as an intermediary reference system. Instead of relying solely on sensors that fail in special environments, the system compares real-time sensor data (lidar point clouds and image data) with pre-stored basic map data to determine vehicle location. This intermediary reference enables positioning to continue functioning in tunnels and heavy rain where traditional GPS and sensor-based positioning fail.
2Measurement precision
If map data is continuously updated, then the map accuracy is improved, but the data processing time and computational resources increase
Solution Approach 1:
The system implements a feedback mechanism by calculating a matching degree value between scan point cloud data and basic map point cloud areas. When the matching degree exceeds a threshold, it triggers a selective update of the basic map. This feedback-based approach ensures map accuracy is improved only when necessary, avoiding continuous unnecessary updates and reducing computational overhead and processing time.
3Productivity
If selective update is performed based on matching degree, then the update efficiency is improved, but the complexity of judgment increases
Solution Approach 1:
The patent transforms the complex judgment of whether to update map data into a quantitative parameter comparison. By calculating a matching degree value (a numerical parameter) between scan data and basic map data, and comparing it against a predefined threshold, the system simplifies the decision-making process. This parameter-based approach improves update efficiency while keeping the judgment logic manageable through clear numerical comparisons.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides accurate vehicle location determination and map updates, enhancing navigation and autonomous driving capabilities even in challenging environments.
Implementation Method 1
The scanning device comprises a lidar sensor, and the lidar sensor is configured to scan the location area to obtain a scan point cloud data of the location data
Data Source
AI summary
A map scanning method including the following step: scanning, by a scanning device, a location area of a vehicle to obtain location data, wherein the scanning device includes a lidar sensor, and the lidar sensor is configured to scan the location area to obtain a scan point cloud data of the location data; comparing, by a processor, the location data with a basic map to obtain a first point cloud area corresponding to the location data in the basic map; Comparing the scan point cloud data with the first point cloud area to calculate a matching degree value; and when the matching degree value is less than an update threshold, updating the first point cloud area according to the scan point cloud data.