Rack Leg-Based Vehicle Position Calibration for Warehouse Odometry
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Solution Overview
Problem
Existing technologies face challenges in accurately localizing and navigating industrial vehicles within warehouse environments, particularly in multilevel racking systems, due to inconsistencies in rack leg positioning and spacing.
Innovation Solution
The implementation of a materials handling vehicle equipped with a camera, odometry module, vehicle position calibration processor, and a drive mechanism, which uses aisle entry identifiers like QR codes and rack leg imaging to generate racking system information, calibrate the vehicle's position, and update odometry data for precise navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional odometry methods are used for vehicle positioning, then the system is simple to implement, but positioning accuracy deteriorates due to accumulated errors
Solution Approach 1:
The patent introduces rack legs as intermediary reference objects between the vehicle and the environment. These rack legs serve as mediators that provide measurable geometric features (spacing, orientation, position) to correct odometry errors. The vehicle camera captures images of rack legs, and the processor uses these intermediary reference points to calculate position corrections, thereby improving positioning accuracy without requiring complex external infrastructure.
Solution Approach 2:
The system implements feedback by continuously comparing expected rack leg positions (based on odometry) with actual captured positions (from camera images). The discrepancy between expected and actual positions generates correction signals that are fed back to adjust the odometry data, creating a closed-loop control system that eliminates accumulated positioning errors over time.
2Measurement precision
If rack leg imaging is implemented for position calibration, then positioning accuracy improves, but processing complexity increases
Solution Approach 1:
The system uses the existing racking system infrastructure (rack legs that are already present in the warehouse) to provide self-service positioning calibration. The rack legs themselves serve as the reference features, eliminating the need for separate calibration markers or external positioning infrastructure. The vehicle's own camera and processor utilize these pre-existing environmental features to perform self-calibration.
Solution Approach 2:
The rack legs serve multiple functions: they are structural support elements of the warehouse racking system and simultaneously serve as positioning reference features for vehicle calibration. This multi-functionality eliminates the need for dedicated positioning infrastructure, reducing overall system complexity while maintaining high calibration accuracy.
3Measurement precision
If odometry error correction is applied continuously, then navigation accuracy improves, but computational load increases
Solution Approach 1:
The system performs odometry correction periodically based on rack leg detection events rather than continuously. The processor captures images at regular intervals or when rack legs are detected, processes these discrete data points to calculate corrections, and applies them to the odometry trajectory. This periodic approach maintains navigation accuracy while significantly reducing computational energy consumption compared to continuous correction.
Data Source
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AI summary
Aspects of the invention relate to a materials handling vehicle (102). The materials handling vehicle (102) includes: a camera (304), an odometry module (29), a vehicle position calibration processor (502), and a drive mechanism configured to move the materials handling vehicle (102) along an inventory transit surface (106). The camera (304) is configured to capture images of (i) an aisle entry identifier (302) for a racking system aisle (70') of a multilevel warehouse racking system (12) and (ii) at least a portion of a rack leg (408) positioned in the racking system aisle (70'). The odometry module (29) is configured to generate materials handling vehicle odometry data. The vehicle position calibration processor (502) is configured to: use the aisle entry identifier (302) to generate racking system information indicative of at least (i) a position of an initial rack leg (408) of the racking system aisle (70') along the inventory transit surface (106) and (ii) rack leg spacing in the racking system aisle (70'), generate an initial position of the materials handling vehicle (102) along the inventory transit surface (106) using the position of the initial rack leg (408), generate an odometry-based position of the materials handling vehicle (102) along the inventory transit surface (106) in the racking system aisle (70') using the odometry data and the initial position of the materials handling vehicle (102), detect a subsequent rack leg (408) using a captured image of at least a portion of the subsequent rack leg (408), correlate the detected subsequent rack leg (408) with an expected position of the materials handling vehicle (102) in the racking system aisle (70') using rack leg spacing from the racking system information, generate an odometry error signal based on a difference between the expected position and the odometry-based position, and update the odometry-based position of the materials handling vehicle (102) using the odometry error signal.