SLAM Pose Estimation Using Plane Feature Constraints
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Solution Overview
Problem
Current SLAM technologies face inefficiencies in calculation resources and time overheads during motion attitude estimation, leading to increased computational burdens and reduced operation efficiency.
Innovation Solution
A data processing method that selects a target three-dimensional point as a constraint to reduce calculation amounts and time overheads, improving the efficiency of the SLAM process by determining a subset of points forming a plane and using these points to locate the intelligent device's pose.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative optimization is performed on constraints constructed from a large quantity of feature points, then measurement precision is improved, but calculation resources and time overheads increase
Solution Approach 1:
The patent extracts only the essential plane feature points from the point cloud data rather than using all feature points. By identifying and selecting only the critical points that define plane features, the method reduces the number of constraints needed for optimization, thereby improving calculation efficiency while maintaining sufficient measurement precision for motion pose estimation.
Solution Approach 2:
The patent segments the point cloud data to identify specific plane feature points separately from other feature points. By dividing the feature extraction process into distinct segments (line features, plane features, and selecting only plane features for constraint construction), the method optimizes the balance between having enough constraints for accurate estimation and keeping the calculation load manageable.
2Measurement precision
If constraints are constructed from all line feature points and plane feature points, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and selects only plane feature points from the complete set of feature points to construct constraints. This extraction process simplifies the constraint construction by focusing only on the most informative subset of features (plane features) while discarding less critical features, thereby reducing system complexity while preserving measurement precision.
Solution Approach 2:
The patent applies different quality standards to different feature types by giving special importance to plane feature points. Instead of treating all feature points equally, the method assigns higher priority to plane features for constraint construction, creating a localized quality differentiation that simplifies the overall constraint system while maintaining estimation accuracy.
Data Source
AI summary
This application relates to the field of artificial intelligence. The technology includes: obtaining data of each three-dimensional point in a first three-dimensional point set, where the data of each three-dimensional point is collected when an intelligent device is in a first position, and the first three-dimensional point set includes a plurality of three-dimensional points located in a first coordinate system; determining a first three-dimensional point subset from the first three-dimensional point set, where the first three-dimensional point subset forms a first plane; calculating a plurality of first distances, which is a distance between a first three-dimensional point and the first position; determining a target first three-dimensional point from the first three-dimensional point subset based on the plurality of first distances; and locating the intelligent device by using the target first three-dimensional point as a constraint to obtain a pose of the first position.