3D Point Cloud Place Recognition via Local Feature Extraction
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Solution Overview
Problem
Current 3D point cloud-based place recognition methods face challenges in feature extraction and retrieval, particularly in real-time and large-scale applications, due to computational expense and lack of robustness under varying conditions, and fail to adequately consider local feature extraction and spatial distribution.
Innovation Solution
A method using a deep neural network to extract local features from 3D point clouds, generating global descriptors, and employing a coarse-to-fine matching strategy for efficient place recognition, which includes capturing 3D point clouds, extracting local features, generating global descriptors, constructing a place descriptor map, and recognizing areas using the map.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional point cloud-based retrieval methods are used, then centimeter-level localization accuracy can be achieved, but the process is time-consuming and requires large storage space for high-resolution maps
Solution Approach 1:
The patent segments the point cloud data into local features and global descriptors. Local features capture detailed geometric information for accurate localization, while global descriptors provide overview information for fast retrieval. This segmentation allows the system to achieve centimeter-level accuracy without processing entire high-resolution maps in detail, thus reducing processing time.
Solution Approach 2:
The patent extracts essential features from the point cloud data, specifically local geometric features and global descriptors, while discarding redundant information. By taking out only the necessary features for place recognition and localization, the system achieves accurate results without requiring storage and processing of complete high-resolution maps, thereby reducing time and storage requirements.
2Measurement precision
If global, off-line, high-resolution maps are used for place recognition, then accurate localization can be achieved, but the computational cost and data storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential features from point cloud data - local geometric features and global descriptors - while discarding redundant information. This extraction allows the system to achieve accurate localization without storing and processing complete high-resolution maps, significantly reducing data storage requirements while maintaining localization precision.
Solution Approach 2:
The patent transforms 3D point cloud data into a multi-dimensional feature space by extracting local features and generating global descriptors. This dimensional transformation allows the system to represent spatial information more efficiently, achieving accurate localization with reduced data complexity and lower storage requirements compared to storing original high-resolution maps.
3Reliability
If point cloud-based methods are used instead of image-based methods, then robustness under different lighting conditions is improved, but computational expense increases and real-time performance cannot be guaranteed
Solution Approach 1:
The patent segments point cloud processing into two parts: extracting local features (which captures robust geometric information independent of lighting) and generating global descriptors (which enable fast retrieval). This segmentation allows the system to maintain the robustness of point cloud methods while reducing computational burden through efficient feature representation and retrieval mechanisms.
Solution Approach 2:
The patent performs preliminary feature extraction and global descriptor generation during an offline or pre-processing phase. By preparing feature representations in advance, the system can perform rapid place recognition and localization in real-time without repeatedly processing the entire point cloud data, thus ensuring real-time performance while maintaining robustness.
4Adaptability or versatility
If existing point cloud feature extraction methods are used, then place recognition can be performed, but the discriminability and universality of global features are insufficient
Solution Approach 1:
The patent applies local quality by extracting local geometric features from each point in the point cloud, capturing detailed spatial information specific to each location. These local features are then aggregated to generate global descriptors that inherit the discriminability of local features while providing universal representation. This local-to-global approach ensures both high discriminability and broad universality of the features.
Solution Approach 2:
The patent merges local feature extraction with global descriptor generation into a unified framework. By combining the detailed local geometric information with the overview global descriptors, the system creates feature representations that are both highly discriminative (due to local features) and universally applicable (due to global context), resolving the trade-off between these two properties.
Data Source
AI summary
Various aspects of a systems and method for place recognition based on a 3D point cloud are disclosed herein. A computer-implemented method for place recognition based on a 3D point cloud, comprising: capturing a 3D point cloud of an area in which the mobile agent is traveling; extracting local features of each point in the captured 3D point cloud; generating a global descriptor of each point of the 3D point cloud using a deep neural network, based on the extracted local features; constructing a place descriptor map of the area based on the generated global descriptors; and recognizing the area by using the generated place descriptor map.