3D Map Estimation Using Multi-Camera Pose Fusion
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Solution Overview
Problem
The accuracy of position and orientation estimation in three-dimensional map estimation is reduced due to vehicle motion and object motion, especially when objects are far away, requiring many frames for detection.
Innovation Solution
A three-dimensional map estimation apparatus that selects and combines data from multiple cameras and sensors to estimate position and orientation, using visual SLAM and coordinate conversion to enhance accuracy and detect obstacles based on a weighted sum of estimation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual SLAM is used for position and orientation estimation using a single camera, then the system complexity is low, but the accuracy of position and orientation estimation is reduced due to vehicle motion and object motion
Solution Approach 1:
The patent combines multiple cameras and sensors to estimate position and orientation by merging their respective estimation results. This integration of multiple data sources improves measurement accuracy while managing system complexity through coordinated processing of multiple inputs.
2Measurement precision
If multiple cameras and sensors are used to improve position and orientation estimation accuracy, then the accuracy increases, but the device complexity increases
Solution Approach 1:
The system uses multiple cameras and sensors that can serve multiple functions - each device contributes to position estimation, orientation estimation, and ultimately three-dimensional map construction. This multi-functionality approach improves accuracy while justifying the increased device complexity through enhanced capabilities.
Solution Approach 2:
The system calculates multiple estimation results from different cameras and sensors, then uses weighted summation based on reliability assessments. This feedback mechanism where estimation results are evaluated and combined improves overall measurement precision while managing the complexity of having multiple devices through systematic integration.
3Measurement precision
If many frames are used to detect motion of distant objects, then the detection accuracy improves, but the loss of time increases
Solution Approach 1:
The patent combines estimation results from multiple cameras and sensors to detect motion of distant objects. By merging data from multiple sources simultaneously, the system achieves accurate motion detection without requiring many sequential frames, thus reducing time loss while maintaining detection accuracy.
Data Source
AI summary
According to one embodiment, a three-dimensional map estimation apparatus includes a processor that selects an imaging apparatus from a plurality of imaging apparatuses and then estimates a position and orientation for a moving object on which the selected imaging apparatus is mounted based on images captured by the selected imaging apparatus. The processor outputs a first position and orientation estimation result for the moving object based on images from selected imaging apparatuses. The processor calculates a second position and orientation estimation result indicating an estimated position and orientation for the moving object using the first position and orientation estimation result. The processor estimates a three-dimensional map for the surroundings of the moving object based on the second position and orientation estimation result.