Warehouse Vehicle Ceiling-Feature Navigation Under Variable Light
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Solution Overview
Problem
Industrial vehicles navigating warehouses face challenges in distinguishing between ceiling lights and skylights under varying illumination conditions, which affects their localization and navigation accuracy.
Innovation Solution
An industrial vehicle equipped with a camera and navigation module that creates a Gaussian scale space pyramid from ceiling images, calculates Hessian response traces, and builds a multiscale candidate graph to identify and differentiate between ceiling lights and skylights, enabling illumination-invariant feature detection and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature detection methods are used to identify ceiling lights and skylights, then the navigation system can operate with simpler processing, but the accuracy of distinguishing between ceiling lights and skylights deteriorates under varying illumination conditions
Solution Approach 1:
The patent segments the image processing into multiple distinct stages: Gaussian scale space pyramid construction, Hessian response calculation, candidate detection, and classification. Each stage processes specific features independently, allowing the system to achieve high accuracy in distinguishing ceiling lights from skylights while maintaining organized complexity through modular processing steps.
Solution Approach 2:
The patent transitions from analyzing images at a single scale to analyzing them across multiple scales using a Gaussian scale space pyramid. This multi-scale approach adds a dimensional aspect to feature detection, enabling the system to identify and distinguish between ceiling lights and skylights regardless of their size or illumination conditions, thereby improving measurement precision.
2Measurement precision
If simple image processing is used, then the processing speed is faster, but the localization accuracy deteriorates due to inability to distinguish between ceiling lights and skylights
Solution Approach 1:
The patent performs preliminary actions by pre-constructing the Gaussian scale space pyramid and pre-calculating Hessian responses before actual feature detection. This preparation work is done once and reused across multiple detection operations, reducing the processing time required for each individual localization task while maintaining high localization accuracy through sophisticated multi-scale analysis.
Solution Approach 2:
The patent extracts only the most relevant features (Hessian response values at specific scales) from the full image data for classification purposes. By focusing computational resources on extracting and analyzing only the critical discriminative features rather than processing entire images, the system achieves high localization accuracy with reduced processing time.
3Reliability
If the system processes all detected features equally, then the processing is simpler, but the reliability of navigation deteriorates due to false identification of features under varying illumination
Solution Approach 1:
The patent applies different processing and classification criteria to different types of features based on their local characteristics. Ceiling lights and skylights are distinguished using scale-specific Hessian response patterns that are unique to each feature type. This localized, feature-specific approach enhances navigation reliability by reducing false identifications while managing complexity through targeted analysis.
Solution Approach 2:
The patent changes parameters (scale, orientation, Hessian response thresholds) adaptively based on the local characteristics of detected features. By adjusting these parameters according to the specific context of each feature detection task, the system improves navigation reliability under varying illumination conditions while maintaining algorithmic organization through systematic parameter management.
Data Source
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AI summary
An industrial vehicle is provided comprising a drive mechanism, a steering mechanism, a vehicle controller, a camera, and a navigation module. The camera is communicatively coupled to the navigation module, the vehicle controller is responsive to commands from the navigation module, and the drive mechanism and the steering mechanism are responsive to commands from the vehicle controller. The camera is configured to capture an input image of a warehouse ceiling comprising elongated skylights, isolated ceiling lights, and/or active optical targets. The navigation module is configured to distinguish between the ceiling lights and the skylights and send commands to the vehicle controller for localization, or to navigate the industrial vehicle through the warehouse based upon valid ceiling light identification, valid skylight identification, valid active target identification, or combinations thereof.