Labeled 3D Point Cloud Localization With Camera-Based Object Registration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing 3D point cloud labeling systems face inaccuracies and high costs due to the need for expensive lidar sensors, making it challenging to achieve accurate localization using less accurate sensors like cameras in autonomous vehicles.
Innovation Solution
A point cloud management system that generates and uses labeled 3D point clouds for localization, employing a server with modules like a point cloud generator, image capturer, object identifier, and localizer to collect and label environmental data from various sensors, including cameras, for consistent and accurate object identification and vehicle localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive lidar sensors are used for accurate localization, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent creates a detailed 3D point cloud map of the environment that serves as a reference model. This digital map is then used to localize the vehicle by comparing current sensor data against the pre-created map, replacing the need for expensive lidar sensors while maintaining localization accuracy.
Solution Approach 2:
The system performs preliminary mapping of the environment to create a detailed 3D point cloud map before localization is needed. This pre-created map contains labeled objects and geometric features that can be used for subsequent localization operations, allowing cheaper sensors to achieve accurate positioning.
2Measurement precision
If manual labeling of 3D point clouds is performed, then object identification accuracy is improved, but productivity decreases
Solution Approach 1:
The system uses automated algorithms to label objects within the 3D point cloud data. The object identification module automatically detects and labels features such as buildings, roads, and other environmental elements without requiring manual intervention, thereby maintaining high labeling accuracy while significantly improving processing speed.
Solution Approach 2:
The patent replaces manual labeling processes with automated computer vision and machine learning algorithms. These computational systems automatically identify and label objects in the point cloud data, substituting human labor with algorithmic processing to achieve both accuracy and efficiency.
3Measurement precision
If detailed map information is provided for accurate localization, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of localization into distinct modules: point cloud generation, object identification, map creation, and localization computation. Each module handles a specific aspect of the process, making the overall system more manageable and easier to implement with cheaper sensors while maintaining high localization accuracy.
Data Source
AI summary
A point cloud management system provides labels for each point within a point cloud map. The point cloud management system also provides a method to localize a vehicle using the labeled point cloud. The point cloud management system identifies objects within a scene using an obtained image. The point cloud management system labels the identified objects to register the identified objects against the point cloud. The registration of the objects is then used to localize the vehicle.