UAV LiDAR Point Cloud Visualization for Real-Time Mapping
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Solution Overview
Problem
Existing mapping technologies require complex environments for scan-matching, limiting their ability to effectively map diverse target environments, and they often rely on post-processing, which delays real-time visualization and analysis.
Innovation Solution
A system for real-time mapping using a movable object equipped with a scanning sensor and positioning sensor, which generates and visualizes LiDAR-based maps in real-time, allowing for geo-referencing and outlier removal, and outputs data as a LiDAR Data Exchange File (LAS) for integration with third-party tools, regardless of environment complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing mapping technologies use scan-matching methods, then mapping accuracy in simple environments is improved, but the ability to map diverse and complex target environments deteriorates
Solution Approach 1:
The system changes the fundamental parameters of the mapping approach by transitioning from scan-matching algorithms to LiDAR-based direct mapping. This parameter change enables the system to achieve high mapping accuracy across diverse environments without relying on environment-specific matching techniques, thereby resolving the contradiction between mapping precision and environmental adaptability.
2Manufacturing precision
If post-processing is used for map generation, then mapping thoroughness is improved, but real-time visualization capability deteriorates
Solution Approach 1:
The system performs preliminary mapping actions during the data collection phase itself, generating and visualizing maps in real-time as the movable object traverses the environment. This preliminary action eliminates the need for separate post-processing steps, thereby maintaining mapping thoroughness while eliminating visualization delays.
Solution Approach 2:
The mapping process continues uninterrupted during data collection, with real-time visualization occurring continuously throughout the mission. This continuous useful action ensures that both thorough mapping and real-time feedback are achieved simultaneously, resolving the time loss contradiction.
3Measurement precision
If high-density point cloud data is collected, then mapping detail and quality are improved, but data transmission and processing load deteriorates
Solution Approach 1:
The system extracts and removes outliers from the point cloud data during collection, separating useful mapping information from noisy or erroneous data points. This extraction process reduces the effective data volume that needs to be transmitted and processed while preserving the high-detail mapping quality of valid points.
Solution Approach 2:
The system collects high-density point cloud data (excessive action) but immediately processes and filters it to retain only the essential mapping information. This approach ensures comprehensive mapping detail is captured while the subsequent filtering reduces the final data volume for transmission and storage.
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 real-time visualization and high-density mapping during missions, allowing for immediate detection of unscanned areas and efficient data transmission, with the ability to generate maps in complex environments and download them post-mission, facilitating various applications like construction and surveying.
Implementation Method 1
A system for real-time mapping using a movable object equipped with a scanning sensor and positioning sensor, which generates and visualizes LiDAR-based maps in real-time
Data Source
AI summary
Techniques are disclosed for real-time mapping in a movable object environment. A system for real-time mapping in a movable object environment, may include at least one movable object including a computing device, a scanning sensor electronically coupled to the computing device, and a positioning sensor electronically coupled to the computing device. The system may further include a client device in communication with the at least one movable object, the client device including a visualization application which is configured to receive point cloud data from the scanning sensor and position data from the positioning sensor, record the point cloud data and the position data to a storage location, generate a real-time visualization of the point cloud data and the position data as it is received, and display the real-time visualization using a user interface provided by the visualization application.