Industrial Truck Camera-LiDAR Positioning for Precise Load Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for autonomous and assisted driving of industrial trucks struggle to provide precise positioning with respect to loading/unloading apparatuses, especially in dynamic environments, due to limitations in navigation systems like GNSS and marker-based methods, which suffer from occlusion and orientation issues, and high data processing requirements of depth sensors or LIDARs.
Innovation Solution
A system combining a video camera and LIDAR device to generate and process image and point cloud data, allowing for precise recognition and alignment with target objects by associating point cloud data with recognized objects, using stored models for verification, and determining distances based on LIDAR data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GNSS and IMU navigation systems are used, then the industrial truck can navigate the industrial environment, but the positioning precision is insufficient for loading/unloading operations
Solution Approach 1:
The patent combines multiple sensing systems (GNSS/IMU for global navigation with camera/LIDAR for local precision positioning) to achieve both global navigation capability and local precision positioning for loading/unloading operations. The control unit integrates data from all sensors to provide comprehensive positioning that works across different operational contexts.
Solution Approach 2:
The patent introduces optical markers as intermediary reference points that bridge the gap between global navigation systems and local precision requirements. These markers serve as intermediate references that cameras and LIDAR can detect to establish precise relative positioning between the truck and loading/unloading apparatus.
2Measurement precision
If optical markers are applied to target objects for precise positioning, then the positioning accuracy improves, but the system complexity increases due to marker installation and maintenance requirements
Solution Approach 1:
The system uses existing structural features of the industrial environment (loading/unloading apparatus, warehouse infrastructure) as natural references rather than requiring separate installed markers. The camera and LIDAR detect these existing features to establish positioning, making the system self-sufficient without external marker installation.
Solution Approach 2:
The camera and LIDAR system serves multiple functions: global navigation, local positioning, obstacle detection, and alignment with loading/unloading apparatus. This multi-functional approach eliminates the need for separate marker systems while maintaining precision positioning capabilities.
3Measurement precision
If depth sensors or LIDARs are used to provide precise positioning data, then the positioning precision improves, but the data processing requirements increase significantly
Solution Approach 1:
The patent segments the positioning task into two parts: global positioning handled by GNSS/IMU for low-processing requirements, and local relative positioning handled by camera/LIDAR for high-precision requirements. This segmentation allows the system to process data selectively based on operational needs, reducing overall processing load.
Solution Approach 2:
The system uses LIDAR to scan the entire environment for comprehensive data, but only processes and analyzes the data within the region of interest where loading/unloading operations are needed. This partial processing approach maintains high positioning precision for critical areas while reducing overall data processing requirements.
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 precise and reliable positioning of industrial trucks relative to target objects, reducing the need for environmental markers and improving alignment accuracy during loading/unloading operations.
Implementation Method 1
a LIDAR device configured to generate point cloud data relating to the visual field
Implementation Method 2
a video camera configured to generate image data relating to a visual field
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
An industrial truck having an autonomous and/or assisted driving function for positioning the industrial truck with respect to a target object, comprising: means for handling a load, a video camera configured to generate image data relating to a visual field, a LIDAR device configured to generate point cloud data relating to the visual field, and control means connected with the video camera in order to receive image data generated by the video camera and connected with the LIDAR device to receive point cloud data generated by the LIDAR device. The control means is configured to: acquire image data from the video camera and point cloud data from the LIDAR device relating to the same visual field; recognise the target object in an image by image processing applied to the acquired image data, wherein the control means is configured to identify a region of interest, ROI, in the image in which the target object is included; associate the acquired point cloud data within the ROI with the target object recognised in the image, including selecting a subset of the plurality of points of the point cloud within the ROI that corresponds to the target object; and control the autonomous and/or assisted driving function based on the point cloud data associated with the recognised target object. (fig. 1)