Tile-Based Point Filtering for Phantom-Free Domestic Appliance Mapping
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Solution Overview
Problem
Existing methods for creating environmental maps using non-contact sensors in household appliances, such as LiDAR, suffer from phantom points caused by bright light or transparent materials, leading to inaccurate mapping and positioning.
Innovation Solution
An improved method involving dividing the environment into tiles and applying a normal distribution transform (NDT) to discard points that do not form a straight line or deviate significantly from an average location, using Mahalanobis distance to filter out phantom points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-contact sensors (LiDAR, radar) are used to scan the environment, then the robot can detect obstacles and create environmental maps, but phantom points appear in the measurements causing inaccurate mapping and positioning
Solution Approach 1:
The environment is divided into multiple tiles of predetermined size, and points within each tile are evaluated independently. This segmentation allows localized filtering of phantom points without affecting the entire map, improving measurement precision while maintaining mapping reliability.
Solution Approach 2:
Different filtering criteria are applied based on local characteristics of point distributions. Points forming straight lines (likely real obstacle boundaries) are preserved, while points with random distributions (phantom points) are discarded. This local quality approach enhances measurement accuracy without compromising mapping reliability.
2Measurement precision
If measurement data is filtered to remove phantom points, then mapping accuracy improves, but processing complexity increases due to additional analysis steps
Solution Approach 1:
By dividing the environment into tiles and processing points locally within each tile, the computational complexity is reduced compared to analyzing all points globally. This segmentation enables efficient implementation of filtering algorithms while maintaining high mapping accuracy.
Solution Approach 2:
The invention changes the parameter of evaluation from individual point analysis to local distribution analysis within tiles. By using statistical properties of point distributions (such as linearity tests), the processing becomes more efficient while achieving better filtering of phantom points.
3Reliability
If points forming straight lines are preserved and other points are discarded, then phantom points are removed effectively, but some valid obstacle points may be incorrectly discarded
Solution Approach 1:
The filtering criterion is applied locally within each tile rather than globally, allowing preservation of straight-line patterns that represent real obstacles while removing random phantom points. This local approach maintains obstacle detection accuracy while improving phantom point removal efficiency.
Solution Approach 2:
The invention applies a strict straight-line criterion that may discard some non-collinear valid points, but this partial loss is acceptable given the significant improvement in removing phantom points. The tile-based approach limits the impact of such discards to local regions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the accuracy and reliability of environmental mapping and positioning by effectively removing phantom points, ensuring precise identification of obstacles and improving appliance navigation.
Implementation Method 1
transmitting a measurement signal into an environment of the household appliance and receiving reflections of the measurement signal
Data Source
Figure 1
Figure 2
Figure 3a~3b
AI summary
A first method for creating a map of the surroundings for a mobile domestic appliance comprises the steps of transmitting a measurement signal to the surroundings of the domestic appliance and receiving reflections of the measurement signal; determining points in the surroundings to which a reflection of the measurement signal is assigned; determining a local distribution of the points in the surroundings; discarding the points if correspondence of the distribution to a straight line fails to reach a predefined degree; and creating the map of the surroundings on the basis of the remaining points. In a second method, points whose distance from an average location of the points exceeds a predefined value are discarded The surroundings can be divided into tiles (110), wherein a normal distribution transformation (NDT) is determined for the points of a tile (110). The NDT specifies an average location of the points (145) and the variance of the points (145). In two dimensions, the variance comprises two eigenvectors (310, 315), for each of which an eigenvalue can be determined which describes how much variance is contained in this eigenvector. A quotient from the larger eigenvalue and the smaller eigenvalue describes whether the variances are distributed equally or unequally. If, however, the quotient is 10 or more, for example, a significantly large number of points (145) of the tile (110) can thus lie along a straight line.