LIDAR Beam Path Contamination Detection Using Measured Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing LIDAR systems face challenges in effectively recognizing and addressing contamination on their beam paths, which can impair their functionality, without requiring additional hardware and relying solely on software-based methods using existing LIDAR measured data.
Innovation Solution
The LIDAR system utilizes existing LIDAR measured data to recognize contamination by analyzing point cloud data, including near and far points, and comparing actual transmittance with a setpoint transmittance to trigger cleaning mechanisms when necessary, differentiating between internal and external contaminants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional hardware is added to detect contamination, then contamination detection capability is improved, but device complexity increases
Solution Approach 1:
The LIDAR system uses its existing light transmitting unit and light receiving unit for both their primary function (scanning surroundings and measuring distances) and for contamination detection. By analyzing the same LIDAR measured data for both purposes, the system achieves multi-functionality without adding dedicated detection hardware, thus resolving the contradiction between improved detection capability and increased device complexity
Solution Approach 2:
The LIDAR system performs self-diagnosis by using its own operational data to detect contamination on its beam path. The system monitors its own performance degradation through analysis of reflected light characteristics, enabling self-service contamination detection without external monitoring equipment, thereby avoiding increased device complexity while maintaining detection capability
2Loss of time
If LIDAR measured data is used for contamination recognition, then loss of time is reduced, but measurement precision may be affected
Solution Approach 1:
The system continuously collects LIDAR measured data during normal operation, preparing the data in advance for contamination analysis. By maintaining a history of measured data and pre-processing it, the system can quickly perform contamination detection when needed without requiring separate measurement time, thus reducing time loss while preserving detection accuracy through thorough data analysis
Solution Approach 2:
The system uses feedback from LIDAR measured data to continuously monitor beam path conditions. By analyzing changes in reflected light intensity and characteristics over time, the system can detect contamination trends and trigger cleaning operations proactively, balancing quick response time with accurate detection through iterative data analysis
3Reliability
If cleaning is triggered based on transmittance comparison, then reliability is improved, but productivity decreases
Solution Approach 1:
The system monitors transmittance as a key parameter and triggers cleaning only when it falls below a predetermined threshold. By dynamically adjusting the cleaning decision based on actual transmittance measurements rather than using fixed schedules, the system maintains high reliability by cleaning only when necessary, avoiding unnecessary cleaning operations that would reduce productivity
Solution Approach 2:
The system performs partial cleaning actions only when contamination actually impacts performance (when transmittance drops below threshold). Rather than performing frequent full cleaning cycles, the system applies cleaning selectively and only to the extent needed to restore proper function, thus maintaining reliability while minimizing productivity loss from cleaning 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
This approach allows for efficient recognition and differentiation of internal and external contaminants, ensuring reliable LIDAR operation by utilizing existing data for contamination detection and enabling automatic cleaning when needed.
Implementation Method 1
the distance to objects that reflect the light is measured
Implementation Method 2
A LIDAR system (LIDAR=Light Detection and Ranging) is a three-dimensional optical measuring system
Implementation Method 3
each artefact in the light path, i.e., in the beam path, influences the measured result of the LIDAR system
Implementation Method 4
particles, for example, in the form of rain, dirt, oil, etc., may settle on the cover element
Data Source
AI summary
A LIDAR system. The LIDAR system includes a light transmitting unit and a light receiving unit. A beam path is formed between the light transmitting unit and the light receiving unit of the LIDAR system, in order to optically scan surroundings of the LIDAR system during the operation of the LIDAR system. The LIDAR system is configured to recognize a contamination of the beam path, based on LIDAR measured data, which have been obtained during the optical scanning of the surroundings. A method for recognizing a contamination of a beam path of a LIDAR system is described, including recognizing the contamination by the LIDAR system based on LIDAR measured data, which have been obtained during an optical scanning of surroundings of the LIDAR system.