Vehicle Point Cloud Positioning Without GPS at Startup
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Solution Overview
Problem
Existing vehicle positioning systems, relying on GPS or GPS and INS, face inaccuracies when GPS signals are poor or absent, leading to failures in determining initial positions, which affects automatic driving processes.
Innovation Solution
A method using laser point cloud data to match reference and target point cloud data from a vehicle's current and preset starting points, employing transformation matrices to determine accurate coordinate and orientation information, including down-sampling to enhance matching efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS or GPS and INS combined devices are used for vehicle positioning, then positioning can be achieved in general conditions, but positioning accuracy deteriorates when GPS signals are poor or absent
Solution Approach 1:
The patent introduces point cloud data as an intermediary medium for positioning. Instead of directly relying on GPS signals, the system uses laser point cloud data from the current environment and matches it with pre-stored point cloud maps to determine vehicle position. This intermediary approach allows positioning to continue when GPS signals are unavailable, resolving the contradiction between reliability and accuracy.
Solution Approach 2:
The patent replaces the GPS satellite-based electromagnetic signal system with a ground-based laser point cloud matching system. By substituting the mechanical/electromagnetic GPS infrastructure with a local point cloud comparison approach, the system achieves positioning accuracy independent of GPS signal quality, thereby improving reliability without sacrificing precision.
2Measurement precision
If complete point cloud data is used for matching, then positioning accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the point cloud data into multiple sections or regions for processing. Instead of performing matching operations on the entire point cloud dataset at once, the system divides the data into manageable segments, processes them separately, and combines the results. This segmentation maintains matching accuracy while significantly reducing computational complexity and processing time.
Solution Approach 2:
The patent applies partial action by selectively processing only the most relevant portions of point cloud data that contain distinctive features for matching. Rather than processing all point cloud data equally, the system identifies and focuses computational resources on key regions that provide the most information for accurate positioning, thereby achieving high accuracy with reduced computational burden.
Data Source
AI summary
This disclosure relates to a position determination method, device, system, and computer-readable storage medium, and relates to the field of computer technologies. The method of the present disclosure includes acquiring laser point cloud data measured at a current position point of a vehicle as reference point cloud data, and acquiring point cloud data corresponding to a preset starting point of the vehicle in a point cloud map as target point cloud data; matching the target point cloud data with the reference point cloud data to determine a transformation matrix between the target point cloud data and the reference point cloud data; and determining coordinate information of the current position point according to coordinate information of the preset starting point and the transformation matrix.