Vehicle Localization Using Overhead Feature Pairs
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Solution Overview
Problem
Industrial vehicles in warehouses face challenges in localization and navigation due to insufficient detection of unique constellations or patterns of ceiling lights, leading to errors in determining their location, especially when they become lost or when camera data is insufficient to correct odometry errors.
Innovation Solution
A system equipped with a camera and vehicular processors that captures overhead features, forms pairs of two-dimensional UV space information with three-dimensional global feature points, calculates vehicle poses, and uses a global localization algorithm to determine the best estimate pose and navigate the vehicle, incorporating odometry to update and validate the vehicle's position, and includes a method to recover from lost states by re-associating camera data with mapped features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional localization methods are used in warehouses, then the system structure remains simple, but the localization reliability deteriorates when ceiling light patterns are insufficient or when vehicles become lost
Solution Approach 1:
The patent transforms the localization problem from 2D image space to 3D global space by creating associated feature pairs that link 2D UV coordinates with 3D global feature points. This dimensional transformation enables more accurate pose calculation and resolves localization failures in environments where conventional 2D methods fail due to insufficient ceiling light patterns
Solution Approach 2:
The patent introduces an intermediary validation mechanism that checks the quality of localization results before accepting them. The validation process verifies whether sufficient unique feature pairs were detected and whether the calculated pose is reliable, preventing propagation of erroneous localization data and improving overall system reliability
2Measurement precision
If more ceiling light features are detected to improve localization accuracy, then the measurement precision improves, but the difficulty of detecting and measuring increases due to insufficient or ambiguous patterns
Solution Approach 1:
The patent segments the localization task into distinct stages: feature detection in 2D image space, creation of associated feature pairs linking 2D and 3D coordinates, and pose calculation from validated pairs. This segmentation allows each stage to be optimized independently and handles cases where individual features may be ambiguous or insufficient
Solution Approach 2:
The patent changes the parameter representation from raw 2D image coordinates to associated feature pairs combining 2D UV coordinates with 3D global feature points. This parameter transformation enriches the information content and enables more precise pose determination even when the number of detectable ceiling lights is limited
3Duration of action of moving object
If odometry data is used to track vehicle movement, then the navigation continuity is maintained, but errors accumulate over time leading to localization drift
Solution Approach 1:
The patent implements a feedback mechanism where ceiling light feature detection provides periodic corrections to the odometry-based position estimates. By continuously comparing the odometry-predicted position with positions derived from visual features, the system corrects accumulated drift and maintains long-term navigation accuracy while preserving continuous operation
Data Source
AI summary
A method for vehicle positioning or navigation utilizing associated feature pairs may include creating a plurality of associated feature pairs by retrieving an initial set of camera data from the camera comprising two-dimensional UV space information, forming pairs from the UV space information, and associating each pair from the UV space information with pairs from each of the plurality of three-dimensional global feature points of the industrial facility map, calculating a best estimate poses from calculated vehicle poses of the associated feature pairs, using an accumulated odometry to update the best estimate pose to a current localized position and setting a seed position as the current localized position. The navigation of the materials handling vehicle is tracked and/or navigated along the inventory transit surface navigate in at least a partially automated manner utilizing the current localized position.