Warehouse Ground Markings for Camera-Based Indoor Positioning
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Solution Overview
Problem
Self-driving systems face challenges in indoor navigation due to the inability of GPS signals to penetrate building roofs and walls, and relying solely on computer vision is insufficient for precise positioning in warehouses where all lanes appear similar.
Innovation Solution
An intelligent warehousing technology that includes machine-readable ground markings with horizontal and vertical lines at equal intervals, recognizable by cameras, allowing self-driving systems to determine their location and navigate within a warehouse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS technology is used for positioning self-driving systems, then positioning reliability is improved outdoors, but positioning becomes impossible indoors where GPS signals cannot penetrate building roofs and walls
Solution Approach 1:
The patent introduces ground markings as an intermediary medium between the self-driving system and the positioning system. These markings serve as a mediator that transmits location information from the environment to the camera, enabling indoor positioning without direct GPS signal penetration.
Solution Approach 2:
The patent creates a copied representation of location information by encoding warehouse location IDs into visual patterns on the ground. Instead of directly receiving GPS signals, the system captures images of these ground markings which are copies of the location data, allowing positioning through image recognition rather than direct signal reception.
2Adaptability or versatility
If computer vision alone is used for positioning, then the system can operate indoors without GPS, but positioning precision deteriorates because all lanes within the warehouse look the same to the camera
Solution Approach 1:
The patent applies local quality by making each ground marking have unique visual characteristics specific to its location. Instead of all lanes looking the same, each ground marking contains encoded location ID information that distinguishes it from others, allowing the camera to identify precise locations through these differentiated local features.
Solution Approach 2:
The patent uses visual pattern changes in the ground markings to encode location information. By varying the machine-readable characteristics (such as line patterns, colors, or arrangements) of the ground markings, the system creates distinguishable visual signatures for different locations that the camera can detect and interpret for precise positioning.
3Ease of operation
If traditional road markings are used for navigation, then the system can provide basic navigation, but wear-out tolerance is poor leading to reduced reliability over time
Solution Approach 1:
The patent segments the navigation information into multiple machine-readable characteristics distributed across the ground marking. Instead of relying on a single continuous marking that can wear out, the information is divided into multiple detectable features (lines, patterns, or zones) that can be independently recognized, providing redundancy and improving tolerance to wear and degradation.
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
Enables accurate positioning and navigation of self-driving systems within warehouses by providing a reliable method for determining location and improving wear-out tolerance compared to traditional road markings.
Implementation Method 1
The machine-readable characteristics are recognizable by one or more cameras of a self-driving system
Data Source
AI summary
Embodiments of the present disclosure relates to a warehousing system. The warehousing system includes a first ground marking arranged on a ground surface of a warehouse. The first ground marking includes machine-readable characteristics representing a warehouse location identification number (ID). The machine-readable characteristics include one or more horizontal lines parallelly arranged at equal intervals, wherein the total number of the one or more horizontal lines corresponding to a first information of the warehouse location ID, and one or more vertical lines parallelly arranged at equal intervals, wherein the total number of the vertical lines corresponding to a second information of the warehouse location ID that is different from the first information. The machine-readable characteristics are recognizable by one or more cameras of a self-driving system, and the self-driving system is operable to determine its position on a map of the warehouse based on the warehouse location ID.