High Precision Map Generation via Lidar Pose Splicing
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Solution Overview
Problem
Current methods for generating high precision maps, particularly in large-scale urban scenarios, face challenges due to weak GPS signals and multipath effects, limiting their accuracy and applicability in complex road conditions.
Innovation Solution
A method involving point cloud splicing to determine lidar pose, projecting data into preset two-dimensional areas based on reflection and height values, and self-positioning verification to integrate data into a reference map, ensuring centimeter-level accuracy and wider application range.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS/SINS integrated navigation system is used to generate high precision maps, then positioning accuracy can be achieved in open highway scenarios, but the method fails to provide satisfactory accuracy in large-scale urban scenarios with weak GPS signals and multipath effects
Solution Approach 1:
The patent introduces point cloud splicing technology as an intermediary method to bridge the gap between GNSS positioning and map generation. By using lidar point clouds as a mediator, the system can achieve precise positioning and map generation in urban environments where GNSS signals are weak or unavailable, thus resolving the contradiction between positioning accuracy and adaptability to different road conditions
Solution Approach 2:
The patent changes the fundamental parameter from relying on GNSS satellite signals to using active lidar illumination and time-of-flight measurements. This parameter change enables the system to operate independently of GPS signal strength, maintaining high precision mapping capability in both open highway and complex urban environments
2Measurement precision
If point cloud splicing process is performed to obtain lidar pose, then positioning accuracy in urban areas is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing point cloud data to extract key features and pre-calculating transformation parameters during the mapping process. This allows the system to maintain high positioning accuracy while reducing the computational burden during real-time operation, as the complex splicing calculations are performed in advance or in a simplified manner
Solution Approach 2:
The patent segments the point cloud data processing into distinct stages: data acquisition, feature extraction, pose estimation, and map integration. By dividing the complex point cloud splicing process into manageable segments, the system can process urban environment data more efficiently while maintaining positioning accuracy
3Reliability
If self-positioning verification is performed on the map, then map quality and reliability are ensured, but processing time increases
Solution Approach 1:
The patent implements a feedback mechanism where the generated map is continuously verified against new lidar measurements through self-positioning checks. This feedback loop ensures map quality and reliability by detecting and correcting errors, while the iterative nature of the process allows for efficient validation without requiring complete reprocessing of all data
Solution Approach 2:
The patent applies partial verification by focusing self-positioning checks on critical map sections or using sampled points rather than verifying every single point cloud measurement. This partial action approach maintains map reliability while significantly reducing the time required for verification compared to exhaustive checking
Data Source
AI summary
A method and an apparatus for generating a high precision map, and a storage medium for generating a high precision map. The method includes: performing a point cloud splicing process on target point cloud data to obtain a lidar pose corresponding to the target point cloud data; projecting the target point cloud data into a preset two-dimensional area based on the lidar pose to generate a map based on a reflection value and a height value; performing a self-positioning verification on the map based on the reflection value and the height value using the target point cloud data; and integrating, if a result of the self-positioning verification satisfies a preset condition, the map based on the reflection value and the height value into a reference map to generate the high precision map.