Warehouse Vehicle Localization Using Ceiling Skylight Centerlines
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Solution Overview
Problem
Industrial vehicles navigating through warehouses face challenges in localization and navigation due to the complexity of ceiling light patterns, particularly with non-rectangular skylights, which existing methods struggle to accurately interpret for precise positioning and path determination.
Innovation Solution
The implementation of a camera-mounted system on industrial vehicles that captures images of ceiling lights, processes them using convex hull determination and Hough transforms to extract centerlines, allowing for accurate vehicle positioning and navigation through warehouses with circular and merged skylights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional localization methods are used with rectangular skylights, then the system works for simple ceiling patterns, but it fails to accurately interpret complex ceiling light patterns including non-rectangular and merged skylights
Solution Approach 1:
The patent transforms the ceiling light detection problem from shape-based recognition to parameter-based analysis by extracting geometric features (convex hull, line segments, angles) and using Hough transform to convert image space features to parameter space representations. This allows the system to handle diverse ceiling light patterns including non-rectangular and merged skylights by analyzing their parametric properties rather than requiring exact shape matching
Solution Approach 2:
The patent applies Hough transform to convert 2D image space coordinates into a different parameter space representation. By transforming the problem from direct image coordinate analysis to parameter space analysis (using line equations and geometric parameters), the system gains the ability to detect and differentiate ceiling light patterns based on their parametric characteristics rather than their raw pixel positions
2Measurement precision
If advanced image processing techniques are implemented to handle complex skylight patterns, then localization accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the complex ceiling light pattern recognition task into distinct processing stages: feature extraction (identifying individual ceiling light elements), convex hull determination (calculating geometric boundaries), line segment extraction (identifying edge characteristics), Hough transform application (converting to parameter space), and centerline determination (calculating central axes). This segmentation allows each sub-task to be optimized independently and processed efficiently
Solution Approach 2:
The patent performs preliminary geometric processing by determining convex hulls and extracting line segments before applying the computationally intensive Hough transform. By pre-processing the image data to identify and simplify geometric features, the system reduces the complexity of the subsequent parameter space transformation and improves overall processing efficiency
Data Source
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AI summary
According to the embodiments described herein, a method for environmental based localization may include capturing an input image of a ceiling comprising a plurality of skylights and a substantially circular light. Features can be extracted from the input image. A convex hull of the raw features can be determined, and a preferred set of lines from the raw features can be selected utilizing the convex hull. A centerline of the skylight can be determined from the preferred set of lines. The substantially circular light of the input image can be transformed into a point feature. A pose and/or position of a vehicle can be determined based upon the centerline of the skylight and the point feature.