UAV Pose Estimation via Point Line Plane Feature Fusion
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Solution Overview
Problem
Current pose estimation methods for unmanned aerial vehicles face challenges such as low positioning accuracy, weak anti-interference capability, and high equipment costs, particularly in weak texture environments and under GPS interference or absence.
Innovation Solution
A pose estimation method using RGB-D cameras to extract point, line, and plane features through a fusion approach, constructing a plane parameter space, inverse octree structure, and association graphs to enhance accuracy and robustness, and compensating with linear or point features when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If feature point information is used for pose estimation, then the method is simple to implement, but positioning accuracy decreases in weak texture environments
Solution Approach 1:
The patent merges multiple feature extraction methods (point features, line features, and plane features) into a unified pose estimation system. By combining these different feature types and their corresponding extraction algorithms, the system achieves both simplicity in implementation and high accuracy in various environments, including weak texture areas where traditional point-based methods fail.
Solution Approach 2:
The patent creates a universal pose estimation system that can handle multiple environment types through multi-functional feature extraction. The system includes point feature extraction for general environments, line feature extraction for structured environments, and plane feature extraction for weak texture environments, making it universally applicable across different scenarios.
2Measurement precision
If laser SLAM is used for pose estimation, then positioning accuracy is improved, but equipment cost and installation complexity increase
Solution Approach 1:
The patent replaces expensive laser radar equipment with a cost-effective RGB-D camera system. By using computational methods (point-line-plane feature fusion) instead of hardware-intensive laser scanning, the system achieves comparable or superior positioning accuracy while dramatically reducing equipment cost and installation complexity.
Solution Approach 2:
The patent substitutes the mechanical laser scanning system with an optical-based RGB-D camera system combined with computational geometry algorithms. This replacement eliminates the need for complex laser radar hardware while achieving high positioning accuracy through software-based feature extraction and fusion methods.
3Measurement precision
If external sensors (GPS, UWB) are used for pose estimation, then positioning accuracy is improved, but anti-interference capability decreases in GPS-denied or interfered environments
Solution Approach 1:
The patent introduces visual features (points, lines, planes) as an intermediary between the camera and the environment for pose estimation. This visual intermediary system operates independently of external sensors like GPS, enabling accurate positioning in environments where GPS is unavailable or interfered with, thereby significantly improving anti-interference capability.
Solution Approach 2:
The system uses the camera and its captured images to perform self-positioning through feature extraction and matching. By relying on its own visual sensors and computational algorithms rather than external infrastructure, the system achieves autonomous pose estimation that is inherently resistant to external interference and environmental constraints.
4Reliability
If plane feature extraction is used, then robustness in weak texture environments is improved, but computational complexity increases
Solution Approach 1:
The patent segments the complex task of feature extraction into distinct modules: point feature extraction, line feature extraction, and plane feature extraction. Each module handles specific aspects of the image data independently, making the overall computational process more manageable and efficient while maintaining high robustness in weak texture environments through specialized plane detection algorithms.
Data Source
AI summary
The present invention provides a pose estimation method for an unmanned aerial vehicle based on point, line and plane feature fusion, the method comprises the following steps: S1 extracting an RGB image and a depth map by using an RGB-D camera; S2 constructing a plane parameter space (PPS) according to the depth map, and mapping the depth map from a Cartesian space to the plane parameter space (PPS); S3 constructing an inverse octree structure, comprising: reversely constructing cell nodes for the plane parameter space (PPS) according to an octree rule, and for each cell node, performing fitting by using Gaussian distribution; S4 extracting a plane feature, comprising: extracting the plane feature on the basis of degree of feature extraction algorithm of the inverse octree; S5 extracting a linear feature, comprising: extracting a linear feature on the RGB image by using LSD algorithm detection.