Warehouse Vehicle Localization Using Ceiling Light And Skylight Features
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Solution Overview
Problem
Industrial vehicles navigating through warehouses face challenges in distinguishing between ceiling lights and skylights due to varying illumination levels, which affects their localization and navigation accuracy.
Innovation Solution
The implementation of a navigation module in industrial vehicles that uses a Gaussian scale space pyramid and Hessian response calculations to differentiate between ceiling lights and skylights, employing determinant and trace of Hessian responses to identify valid features and navigate the vehicle accordingly.
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 feature detection process into distinct computational stages: Gaussian scale space pyramid construction, Hessian matrix calculation, determinant computation, and trace computation. Each stage processes intermediate results independently, allowing the system to handle complex illumination variations through systematic decomposition of the detection task.
Solution Approach 2:
The patent extends the feature detection from single-scale analysis to multi-scale analysis by constructing a Gaussian scale space pyramid. This adds the scale dimension to the detection process, enabling the system to identify ceiling lights and skylights across multiple resolution levels, thereby improving distinction accuracy under varying illumination conditions.
2Reliability
If the navigation module processes both determinant and trace of Hessian responses, then feature identification accuracy improves, but computational time and processing load increase
Solution Approach 1:
The patent performs preliminary Gaussian smoothing and scale space pyramid construction before Hessian matrix calculations. This preliminary action prepares the image data in advance, reducing the computational burden during the actual feature detection phase and enabling faster processing of both determinant and trace responses.
Solution Approach 2:
The patent calculates both the determinant and trace of the Hessian matrix, which represents excessive action beyond the minimum required for basic feature detection. However, this partial or excessive computation is performed on pre-processed scale space images, which reduces the overall processing time and balances the trade-off between reliability and time loss.
Data Source
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 characterized by different rates of image intensity change along longitudinal and transverse axial directions, and ceiling lights characterized by a circularly symmetric rate of image intensity change. 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, or both.