Ceiling-Feature Localization for Industrial Vehicle Navigation
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Solution Overview
Problem
Industrial vehicles navigating in environments with varying illumination levels face challenges in accurately detecting features for localization due to illumination variability, which affects their ability to navigate reliably.
Innovation Solution
The industrial vehicle is equipped with a camera and navigation module that creates a Gaussian scale space pyramid from input images of the warehouse ceiling, calculates a trace of Hessian response, and builds a multiscale candidate graph to identify skylights, ceiling lights, and active targets, enabling illumination-invariant feature detection and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional feature detection methods are used in environments with varying illumination levels, then the system can operate with simple detection algorithms, but the detection accuracy and reliability deteriorate due to illumination variability
Solution Approach 1:
The patent segments the feature detection process into multiple stages: creating a Gaussian scale space pyramid to detect features at multiple scales, calculating Hessian response to identify corner-like features, and building a multiscale candidate graph to select robust features. This segmentation allows the system to detect illumination-invariant features by analyzing the image at different scales and selecting features that maintain consistency across scales, thereby improving detection accuracy and navigation reliability in varying illumination conditions.
2Reliability
If illumination-invariant feature detection is implemented, then navigation reliability improves in varying illumination levels, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by first creating a Gaussian scale space pyramid from the input image before proceeding to Hessian response calculation. This preliminary step transforms the original image into a multi-scale representation, allowing subsequent feature detection to operate on pre-processed data that already emphasizes scale-invariant structures. This preliminary transformation simplifies the overall computational process while maintaining illumination invariance, as the Gaussian blurring and scaling are performed once rather than repeatedly during feature evaluation.
Solution Approach 2:
The patent introduces another dimension by transforming the 2D image problem into a 3D volume through the Gaussian scale space pyramid. Instead of analyzing features at a single scale, the system creates a pyramid of images at different scales (sigma values), adding the scale dimension to the analysis. This dimensional transformation allows the Hessian response calculation to detect features that are invariant to illumination changes and scale variations, improving reliability while distributing computational load across multiple scales rather than requiring extremely complex single-scale processing.
3Measurement precision
If multiple processing steps are used to create Gaussian scale space pyramid and Hessian response, then feature identification accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies periodic action through the structured, staged processing approach: first creating the Gaussian scale space pyramid with systematically increasing sigma values, then calculating Hessian response at each scale level, and finally building the multiscale candidate graph. This periodic, systematic progression through scales allows the algorithm to efficiently identify features at the appropriate scale without unnecessary computations at all scales, balancing accuracy with processing time by focusing computational effort on the most informative scale levels.
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, 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.