Particle Cloud Localization via Frequency Curve Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining the position of automatically movable devices, such as soil dust collection devices, using SLAM (Simultaneous Localization and Mapping) face challenges with imprecise localization due to high computational intensity and variance in particle scattering, especially when obstacles are present, leading to inaccurate positioning.
Innovation Solution
The method involves projecting particles into a coordinate system, analyzing variance to assess localization accuracy, and using a frequency curve to evaluate the maximum probability for precise positioning, reducing the number of particles needed and computational effort, allowing for more cost-effective electronics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of particles are scattered and evaluated using traditional SLAM methods, then localization coverage is improved, but computational intensity increases and localization precision decreases due to variance in particle scattering
Solution Approach 1:
The patent extracts only the necessary information from particle scattering by analyzing variance along coordinate axes and projecting particles into one dimension. Instead of evaluating all particles in two dimensions, the method extracts positional information along the first coordinate axis by projecting particles and comparing with frequency curves, thereby reducing computational intensity while maintaining localization precision.
Solution Approach 2:
The patent transforms the two-dimensional particle evaluation problem into a one-dimensional problem by projecting particles along the first coordinate axis. This dimensionality reduction allows the system to evaluate particle positions more efficiently by comparing projected positions with frequency curve maxima, significantly reducing computational requirements while improving localization accuracy.
2Productivity
If traditional particle scattering methods are used, then comprehensive position evaluation is achieved, but the algorithm runs slowly and requires powerful electronics
Solution Approach 1:
The patent replaces the traditional mechanical particle scattering evaluation system with a statistical frequency analysis system. Instead of evaluating each particle's position and orientation in two dimensions, the method substitutes this with frequency curve analysis of projected particle positions, which speeds up the algorithm while maintaining or improving position determination accuracy through statistical maxima identification.
3Reliability
If variance in particle scattering is high, then more particles are needed for accurate localization, but this increases computational effort and device complexity
Solution Approach 1:
The patent changes the evaluation parameter from two-dimensional particle position to one-dimensional projected position frequency distribution. By analyzing the frequency of projected particle positions along the first coordinate axis and identifying maxima in the frequency curve, the system achieves reliable localization even with high particle scattering variance, without increasing device complexity or requiring more particles.
Data Source
Figure 1
Figure 2~4
Figure 5
AI summary
The invention relates to a method for determining the position of an automatically movable device (1), preferably with electrically driven wheels (3), wherein the device (1) is provided with an obstacle detection system (A), wherein a plurality of possible positions and orientations (particles 6) are calculated starting from an assumed first, previously calculated position of the device (1), and according to a corresponding method of the device (1), with regard to the second position then assumed, one of the previously generated particles (6) is assumed as the new location of the device (1) based on the measurement results of the optical units according to a predetermined selection algorithm that takes probability into account.To further improve a method of the type in question, it is proposed that the particles (6) be evaluated such that the largest extent of a calculated particle cloud (7) is overlaid with a first axis (x, y) of a coordinate system (K) and that, starting from this, the deviation of the particles (6) in the direction of the other, second coordinate axis (y, x) is recorded, that furthermore all the particles (6) are projected onto the first coordinate axis (x, y) and a particle distribution thus recorded is overlaid with a frequency curve (H), whereby a maximum (P) of the frequency curve (H) is evaluated as an approximation of the actual position of the device (1).