3D Scanner Projection for Low-Complexity 2D Outdoor SLAM
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional 2D SLAM systems are inadequate for outdoor environments due to their assumption of a flat world, leading to reduced reliability and precision, while 3D SLAM systems increase complexity and computational effort significantly.
Innovation Solution
Converting 3D pixel clouds into 2D pixel clouds for processing with 2D SLAM algorithms, using odometry data to compensate for scanner movement, aligning with gravity, extracting vertical structures, and applying voxel filtering or undersampling to reduce data complexity, thereby enabling efficient localization and mapping with lower computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a 3D SLAM system is used to process outdoor environments with complex structures, then measurement precision and reliability are improved, but device complexity and computational effort increase significantly
Solution Approach 1:
The patent extracts only the essential vertical structure information from the 3D environment while discarding unnecessary horizontal and depth information. This is achieved by projecting 3D point cloud data onto a 2D plane and retaining only points that represent vertical structures (walls, buildings), thereby simplifying the data structure while preserving localization-relevant information.
Solution Approach 2:
The patent transforms 3D spatial data into 2D representation by projecting point clouds onto a vertical plane. This dimensionality reduction converts complex three-dimensional coordinates (x, y, z) into two-dimensional coordinates (x, y) while maintaining the essential vertical structure information needed for outdoor localization, thereby reducing computational complexity.
2Measurement precision
If a 3D scanner is used to capture complex outdoor structures, then measurement precision is improved, but computational effort and processing time increase
Solution Approach 1:
The patent extracts only the necessary vertical structure information from the complete 3D point cloud data. By filtering and projecting points to retain only those representing vertical structures (walls, buildings, towers), the system reduces the volume of data requiring computational processing while maintaining the precision needed for accurate outdoor localization and mapping.
3Measurement precision
If all six degrees of freedom are determined in a 3D SLAM system, then localization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts and focuses only on the essential positional and orientational parameters needed for 2D localization (horizontal position and vertical angle) while discarding unnecessary three-dimensional parameters. This selective extraction of relevant degrees of freedom simplifies the algorithmic complexity while maintaining sufficient accuracy for outdoor vehicle navigation.
Data Source
AI summary
A method for the simultaneous localization and mapping in 2D using a 3D scanner. An environment is scanned with the aid of the 3D scanner in order to generate a three-dimensional representation of the environment in the form of a 3D pixel cloud made up of a multitude of scanned pixels. A two-dimensional representation of the environment in the form of a 2D pixel cloud is subsequently generated from the 3D pixel cloud. The 2D pixel cloud is conveyed to a 2D SLAM algorithm for the generation of a map of the environment and for the simultaneous ascertainment of the current position of the 3D scanner within the map.