LiDAR Trajectory Correction with INS/GPS Fusion for 3D Mapping
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Solution Overview
Problem
Existing methods for generating precise 3D maps using LiDAR data in autonomous vehicles suffer from errors due to the changing position of LiDAR sensors, leading to inaccuracies in road map generation.
Innovation Solution
A trajectory correction method that includes error correction using INS and GPS data fusion, curve fitting with Bezier algorithms, and mapping to minimize errors in 3D point cloud data collected by LiDAR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LiDAR data is collected in real-time from a moving vehicle, then the 3D map can be generated dynamically, but positional errors occur due to LiDAR movement
Solution Approach 1:
The patent applies feedback by using INS and GPS data to continuously monitor and correct the LiDAR position and trajectory. The system receives feedback from sensors about actual vehicle movement and uses this information to compensate for positional errors in the 3D point cloud data, maintaining measurement precision during real-time dynamic map generation.
Solution Approach 2:
The patent introduces INS and GPS systems as intermediary components that mediate between the moving LiDAR and the 3D map generation process. These intermediary sensors provide trajectory information that acts as a bridge to correct positional deviations, allowing real-time generation while maintaining accuracy through intermediate correction steps.
2Measurement precision
If trajectory correction is applied to maintain accuracy, then measurement precision improves, but system complexity increases
Solution Approach 1:
The patent achieves universality by using multi-functional sensor integration where INS and GPS systems serve multiple purposes: navigation, trajectory tracking, and error correction. This multi-functionality reduces the need for dedicated correction hardware, managing system complexity while maintaining high measurement precision through existing sensor capabilities.
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 method effectively reduces errors in 3D map generation by correcting positional changes in LiDAR data, enhancing the accuracy of 3D maps for autonomous driving applications.
Implementation Method 1
The LiDAR may acquire 3D data expressing the distance and shape of an object by emitting a high-power laser pulse and measuring the time of the laser reflected and returned from a target
Implementation Method 2
a precise road map that can provide various information actually needed for autonomous driving is essential
Implementation Method 3
MMS may be generated based on information collected by Global Positioning System (GPS), Inertial Navigation System (INS), and Inertial Measurement Unit (IMU) for acquiring position and posture information of the vehicle body
Data Source
AI summary
A trajectory correction method may include the steps of: correcting, by a map generation device, an error generated due to a change in position targeting 3D point cloud data collected in real time through a LiDAR, of which a position changes in real time; estimating, by the map generation device, an expected trajectory (odometry) related to the change in position on the basis of the corrected 3D point cloud data; applying, by the map generation device, curve fitting to the estimated expected trajectory; and mapping, by the map generation device, the expected trajectory before the curve fitting targeting the expected trajectory to which the curve fitting is applied.