Container Bay Position Mapping Using Camera-LiDAR Fusion
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Solution Overview
Problem
The challenge of automating crane operations over container vessels is hindered by the large variety of container types and the movement of vessels during berthing, requiring accurate and reliable cargo information that is not currently available for autonomous operation.
Innovation Solution
A method utilizing combined image data from cameras and LiDAR data to determine properties of containers, including position, size, and type, by calibrating the camera and LiDAR devices on the crane structure, and using machine learning algorithms for precise container identification and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual operation is used to handle container vessel variety, then operational flexibility is maintained, but automation level remains low and productivity is limited
Solution Approach 1:
The system enables autonomous operation where the crane automatically determines container properties (position, size, type) using onboard cameras and LiDAR sensors, eliminating the need for manual intervention while adapting to different container types through automated recognition and classification algorithms
Solution Approach 2:
The system changes operational parameters dynamically by adjusting crane movements based on real-time container bay mappings and container properties, allowing flexible adaptation to various container configurations while maintaining high automation levels
2Reliability
If detailed cargo information is collected for autonomous operation, then automation capability is improved, but data acquisition complexity and device requirements increase
Solution Approach 1:
The system merges camera imaging data with LiDAR point cloud data to create comprehensive container bay mappings, combining multiple sensor types into a unified autonomous operation system that improves reliability while managing complexity through integrated data processing
Solution Approach 2:
The patent introduces an intermediary processing layer that captures raw sensor data, processes it through coordinate transformations and data fusion algorithms, and outputs refined container property information, mediating between complex sensor inputs and autonomous control requirements
3Productivity
If crane operates at high speed with shorter cycle times, then productivity increases, but precision in container position determination must be maintained
Solution Approach 1:
The system performs preliminary container bay mapping and container property determination before crane operations begin, pre-processing sensor data to establish accurate reference frameworks that enable high-speed operations without sacrificing measurement precision
Solution Approach 2:
The patent replaces traditional mechanical measurement systems with optical (camera) and laser (LiDAR)-based sensing systems, enabling non-contact, high-precision container position and property determination that supports accelerated crane operations
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 high-speed and efficient autonomous crane operations by providing detailed information about container bays, enhancing operational safety and productivity.
Implementation Method 1
receiving first image data from a camera mounted on a structure of a crane, wherein the structure of the crane at least partly extends over the container bay and wherein the first image data are representative of a first image showing at least an area of the container bay
Implementation Method 2
receiving first LiDAR data from a LiDAR device mounted on a structure of the crane, wherein the first LiDAR data are representative of a first point cloud representing at least a part of the area of the container bay
Data Source
AI summary
A method for determining at least one property of a container on a container bay of a container vessel is described. The method comprises receiving first image data from a camera mounted on a structure of a crane, wherein the structure of the crane at least partly extends over the container bay. The first image data is representative of a first image showing at least an area of the container bay in which the container is arranged. The method further comprises receiving first LiDAR data from a LiDAR device mounted on a structure of the crane, wherein the first LiDAR data is representative of a first point cloud representing at least a part of the area of the container bay. The method additional comprises combining the received first image data and the received first LiDAR data, and determining the property of the container depending on the combined data.