Geographic Object Detection Using Image and 3D Point Cloud Fusion
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Solution Overview
Problem
Existing technologies face challenges in creating highly accurate mapping data for autonomous driving by relying solely on point cloud or image data, as they either fail to specify the three-dimensional position or type of geographic objects accurately, leading to misrecognition or overlooking.
Innovation Solution
A geographic object detection apparatus that combines image data and three-dimensional point cloud data to recognize the type of geographic objects and specify their positions, using a geographic object recognition unit and position specification unit for precise detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only point cloud data is used for geographic object extraction, then the three-dimensional position can be specified, but the type recognition becomes difficult
Solution Approach 1:
The patent merges point cloud data and image data into a unified detection system. The point cloud data provides three-dimensional position information while image data provides object type recognition. By combining these two data sources through data fusion techniques, the system achieves both accurate position specification and reliable type recognition simultaneously, resolving the contradiction between the two requirements.
2Loss of information
If only image data is used for geographic object extraction, then the type can be specified, but the three-dimensional position cannot be accurately determined
Solution Approach 1:
The patent combines image data with point cloud data to overcome the limitations of using only image data. While image data excels at object type specification through visual recognition, the integrated point cloud data supplements this with precise three-dimensional position information. The fusion of these complementary data sources enables simultaneous achievement of both objectives.
3Measurement precision
If manual mapping data creation is used, then high accuracy can be achieved, but very high labor costs and temporal costs are required
Solution Approach 1:
The patent implements an automated mapping data creation system that performs detection and mapping tasks autonomously using integrated point cloud and image data processing. The system automatically identifies geographic objects, determines their positions and types, and generates mapping data without human intervention. This self-service capability maintains high accuracy while dramatically reducing labor costs and processing time compared to manual methods.
Solution Approach 2:
The patent replaces the manual mechanical process of mapping data creation with an automated computational system. Instead of human operators manually analyzing data and creating maps, the system uses algorithmic processing of point cloud and image data to automatically generate mapping information. This substitution of mechanical human labor with automated computational mechanisms achieves both high accuracy and improved productivity.
Data Source
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AI summary
A geographic object recognition unit (120) recognizes, using image data (192) obtained by photographing in a measurement region where a geographic object exists, a type of the geographic object from an image that the image data (192) represents. A position specification unit (130) specifies, using three-dimensional point cloud data (191) indicating a three-dimensional coordinate value of each of a plurality of points in the measurement region, a position of the geographic object.