Laser Point Cloud Reflection Value Matching for Driverless Vehicle Positioning
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Solution Overview
Problem
GPS-based RTK positioning in driverless vehicles is prone to errors due to blocked satellite signals and intense multi-path effects in complex environments, leading to inaccurate positioning.
Innovation Solution
A method and system utilizing laser point cloud reflection value matching, where laser point cloud reflection data is acquired, converted into horizontal earth plane projection data, and matched with a predetermined range in a laser point cloud reflection value map to determine the vehicle's position based on matching probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based RTK positioning is used to determine the position of a driverless vehicle, then the positioning system can provide location information, but the positioning accuracy deteriorates when GPS satellite signals are blocked or multi-path effects are prominent
Solution Approach 1:
The patent introduces laser point cloud reflection value data as an intermediary to bridge the gap between GPS positioning and actual vehicle location. By matching laser reflection patterns from the environment with pre-stored point cloud maps, the system can determine vehicle position independently of GPS signals, thus resolving the contradiction between maintaining positioning accuracy and reliability in signal-blocked environments
Solution Approach 2:
The patent changes the positioning parameter from GPS coordinates to laser point cloud reflection characteristics. By using laser reflection intensity values and point cloud spatial distributions as new positioning parameters, the system achieves accurate positioning in environments where traditional GPS parameters fail, thereby improving both measurement precision and reliability
2Measurement precision
If laser point cloud reflection value matching is used for positioning, then positioning accuracy is improved in complex environments, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The patent makes the laser scanner serve multiple functions: both environmental mapping for navigation and positioning determination. By using the same laser point cloud data for both creating environmental models and determining vehicle position, the system achieves high positioning accuracy without proportionally increasing device complexity, as the laser scanner performs dual roles
Solution Approach 2:
The patent creates a copy of the environmental structure in the form of a point cloud reflection value map stored in advance. By comparing real-time laser measurements with this pre-created digital copy of the environment, the system achieves accurate positioning without requiring complex real-time processing infrastructure, thus balancing measurement precision with manageable device complexity
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
This approach provides accurate positioning for driverless vehicles by overcoming the limitations of GPS-based RTK positioning, ensuring precise location determination even in environments with obstructed signals or intense multi-path effects.
Implementation Method 1
acquiring first laser point cloud reflection value data matching a current position of the driverless vehicle, the first laser point cloud reflection value data comprising first coordinates of laser points and laser reflection intensity values corresponding to the laser points
Data Source
AI summary
Disclosed embodiments include a driverless vehicle, and a method, an apparatus and a system for positioning a driverless vehicle. In some embodiments, the method includes: acquiring first laser point cloud reflection value data matching a current position of the driverless vehicle; converting the first laser point cloud reflection value data into laser point cloud projection data in a horizontal earth plane; determining a first matching probability of the laser point cloud projection data in a predetermined range of a laser point cloud reflection value map by using a position of a predetermined prior positioning position in the laser point cloud reflection value map as an initial position; and determining a position of the driverless vehicle in the laser point cloud reflection value map based on the first matching probability. The implementation can accurately position the current position of the driverless vehicle.