Optical Target Localization for Feature-Scarce Indoor Vehicle Navigation
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Solution Overview
Problem
Industrial vehicles in warehouses face challenges in navigation and localization due to the lack of reliable overhead features, especially in environments with insufficient density of extractable features or when traveling outdoors where overhead ceiling features are unavailable.
Innovation Solution
The implementation of a system that includes a camera, vehicular processor, and optical targets with point light sources mounted on a bar, which captures images of overhead features, decodes the light patterns to determine vehicle pose, and navigates the vehicle using this information, allowing for both automated and manual navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional localization methods using overhead ceiling features are used, then navigation is reliable in environments with sufficient overhead features, but localization fails in environments with insufficient density of overhead features or outdoor areas
Solution Approach 1:
The patent transitions from two-dimensional overhead ceiling features to three-dimensional optical targets with vertical extent. The optical targets include a vertical bar structure with light sources at different heights, creating a 3D geometric pattern that can be recognized from multiple angles and distances, thereby solving the limitation of 2D feature recognition in feature-scarce environments.
Solution Approach 2:
The optical targets utilize light sources that emit specific colors or light patterns to create distinctive visual codes. These colored light patterns enable the camera system to differentiate between multiple optical targets and decode their identities, providing robust localization signals in environments where traditional grayscale ceiling features are insufficient.
2Measurement precision
If optical targets with multiple light sources are used, then localization accuracy is improved, but device complexity increases
Solution Approach 1:
The optical target is segmented into multiple light sources positioned at specific locations along a vertical bar. Each light source can be independently controlled to create different light patterns, allowing the system to encode multiple pieces of information (identity, position, orientation) within a single optical target structure, thereby improving localization accuracy without proportionally increasing complexity.
Solution Approach 2:
The optical target structure serves multiple functions simultaneously: it provides geometric reference for pose estimation, encodes identity information through light patterns, and enables distance and orientation measurement through its vertical configuration. This multi-functionality reduces the need for separate systems for each measurement type, offsetting the increased structural complexity with functional integration.
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
This solution enables accurate and reliable navigation of industrial vehicles within warehouses by utilizing overhead optical targets to determine vehicle pose and orientation, enhancing localization accuracy and uptime, even in areas with insufficient overhead features.
Implementation Method 1
The camera may be communicatively coupled to the vehicular processor and captures an input image of overhead features
Implementation Method 2
Each optical target may include a plurality of point light sources mounted on a bar that is configured for attachment to a ceiling
Data Source
AI summary
Vehicles, systems, and methods for navigating or tracking the navigation of a materials handling vehicle along a surface that may include a camera and vehicle functions to match two-dimensional image information from camera data associated with the input image of overhead features with a plurality of global target locations of a warehouse map to generate a plurality of candidate optical targets, an optical target associated with each global target location and a code; filter the targets to determine a candidate optical target; decode the target to identify the associated code; identify an optical target associated with the identified code; determine a camera metric relative to the identified optical target and the position and orientation of the identified optical target in the warehouse map; calculate a vehicle pose based on the camera metric; and navigate the materials handling vehicle utilizing the vehicle pose.