Laser Range Finder Distance Type Evaluation for Mobile Robot Localization
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Solution Overview
Problem
Existing localization methods for mobile robots using laser range finders face challenges in accurately determining distance types due to reflective characteristics of environments, particularly with glass walls, leading to measurement errors and reduced performance.
Innovation Solution
A method that evaluates distance types by extracting preliminary samples, calculating reference distance sets using a ray casting algorithm, and determining the smallest distance error to accurately classify measured distances, thereby improving localization accuracy in environments with glass walls and unknown obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If laser range finder measures distance in environment with glass walls, then localization can be performed, but measurement accuracy deteriorates due to reflective characteristics
Solution Approach 1:
The patent changes the parameter of distance measurement by introducing distance type classification (first type vs. second type). The system identifies whether a measured distance corresponds to a reflective surface (glass wall) or a regular obstacle, and applies different handling strategies for each type, thereby improving localization reliability in environments with glass walls
Solution Approach 2:
The patent applies different processing methods to different portions of the measured distance data. By identifying specific distance measurements that correspond to glass walls (second type distances) and treating them differently from regular obstacle distances (first type distances), the system improves overall measurement precision by addressing local quality differences in the environment
2Measurement precision
If distance type evaluation is performed for all measured distances, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the distance measurement data into two distinct categories: first type distances (regular obstacles) and second type distances (reflective surfaces like glass walls). This segmentation allows the system to apply simplified processing to the majority of first type distances while focusing detailed analysis only on second type distances, thereby improving measurement precision without proportionally increasing computational complexity
Solution Approach 2:
The patent applies partial action by performing comprehensive distance type evaluation only for specific cases (second type distances corresponding to glass walls) rather than for all measured distances. This selective approach maintains measurement precision for critical cases while reducing overall computational complexity by avoiding redundant analysis of routine measurements
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
This method enhances the accuracy of mobile robot localization by accurately determining distance types and reducing measurement errors caused by reflective characteristics, ensuring robust localization in environments with glass walls and unknown obstacles.
Implementation Method 1
The measured value of the laser range finder reflected from the general object is a distance which is measured by a diffuse reflection from an object what the laser beam reaches firstly.
Implementation Method 2
the measured value of the laser range finder is changed depending on various reflection phenomena such as a diffuse reflection, a specular reflection, and a penetration, etc. in the glass wall.
Implementation Method 3
the measured value of the laser range finder is changed depending on various reflection phenomena such as a diffuse reflection, a specular reflection, and a penetration, etc. in the glass wall.
Implementation Method 4
a reference set calculating step for calculating a reference distance set corresponding to each preliminary sample through applying each preliminary sample to a reference distance calculating algorithm which is previously registered
Data Source
AI summary
A method for evaluating the distance type of the measured distance comprises a sample extracting step for extracting a plurality of preliminary samples around a predicted pose; a reference set calculating step for calculating a reference distance set corresponding to each preliminary sample through applying each preliminary sample to a reference distance calculating algorithm which is previously registered, wherein the reference distance set comprises reference distances corresponding to each of a plurality of distance types; a distance type extracting step for extracting a distance type corresponding to each of the reference distance sets based on a smallest distance error among distance errors between each reference distance which compose the reference distance set and the measured distance; and a distance type evaluating step for evaluating a distance type of the measured distance based on the distance type which is extracted in correspondence with each reference distance set.