Sensor Extrinsics Calibration for Warehouse Vehicle Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Materials handling vehicles face operational challenges due to manufacturing or installation tolerances that affect sensor calibration, leading to unreliable localization and navigation in warehouse environments.
Innovation Solution
A system and method for calibrating sensors on materials handling vehicles using a vehicle position processor that generates sensor data, creates a factor graph, and optimizes it to provide calibration outputs, allowing for accurate localization and navigation regardless of the vehicle's location, using image-based or laser-based sensors and techniques like SLAM and GTSAM libraries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manufacturing or installation tolerances are reduced to improve sensor calibration accuracy, then localization precision is improved, but manufacturing cost and complexity increase
Solution Approach 1:
The system changes the parameters of sensor installation by allowing a range of tolerances in roll, pitch, yaw, and position during manufacturing, then compensates for these parameter variations through post-installation calibration that determines actual sensor extrinsics relative to the vehicle coordinate frame
Solution Approach 2:
The calibration system performs preliminary characterization of sensor installation parameters during or after manufacturing, storing baseline extrinsic parameters that are later used to correct localization calculations, thereby preventing the need for tight manufacturing tolerances
2Measurement precision
If sensor calibration is performed at the factory to improve localization accuracy, then measurement precision is improved, but adaptability to different installation locations is reduced
Solution Approach 1:
The system makes the calibration process dynamic by enabling calibration to be performed at multiple stages (factory, distribution center, or end-use location) and allowing recalibration if sensors are relocated or replaced, thereby adapting to different installation scenarios while maintaining accuracy
Solution Approach 2:
The calibration system is designed to be universal by working with any sensor type (cameras, LIDAR, radar) and any installation location, using the same factor graph optimization approach to determine extrinsics regardless of where the sensor is mounted on the vehicle
3Ease of manufacture
If traditional calibration methods requiring site maps are used, then initial setup is simplified, but flexibility for retrofitting and maintenance is reduced
Solution Approach 1:
The system extracts the dependency on pre-existing site maps by using only sensor observations of environmental features and vehicle odometry to build the factor graph and optimize extrinsics, thereby removing the barrier to retrofitting and maintenance while maintaining calibration capability
Data Source
AI summary
Methods and systems for a materials handling vehicle comprising a processor and a sensor to record warehouse features. The processor is configured to generate and extract features from sensor data, create a factor graph (FG) including a sensor extrinsics node (e0), and generate an initial vehicle node (v0), initial sensor frame node (c0), and initial sensor feature node (f0) that comprises a selected extracted feature associated with c0 and v0 in an initial data association. A subsequent vehicle node (v1) is generated based on an accumulated odometry amount, and a subsequent sensor frame node (c1) is generated and associated with e0, v1, and one of f0 or a subsequent sensor feature node (f1) in a subsequent data association. The FG is optimized to provide a sensor calibration output based on the data associations, and the vehicle is navigated based on the sensor calibration output.