LiDAR Alignment Calibration Using Secondary-Light Distribution Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing LiDAR systems face challenges in accurately aligning their transmitter and receiver units during installation and operation, leading to reduced performance due to mechanical and thermal tolerances, which are typically compensated by over-designing with tolerance margins, increasing costs and reducing nominal performance.
Innovation Solution
A method for calibrating and adjusting LiDAR systems by measuring and comparing the distribution of secondary light on a detector unit relative to expected positions, determining deviation values, and applying electrical, mechanical, or optical corrections to align the transmitter and receiver units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tolerance margins are used to compensate for mechanical and thermal tolerances, then reliability is improved, but manufacturing precision deteriorates due to over-design
Solution Approach 1:
The system performs preliminary alignment measurements during operation to detect deviations caused by mechanical and thermal tolerances. By measuring the actual alignment state and comparing it with expected values, the system can compensate for tolerances through electronic adjustment rather than requiring excessive manufacturing precision during production.
Solution Approach 2:
The invention implements a feedback mechanism that continuously monitors the alignment between transmitter and receiver units by measuring secondary light distribution. The measured alignment data is fed back to the control unit, which calculates correction values and applies them to maintain optimal alignment, thereby ensuring reliability without requiring tolerance margins in the mechanical design.
2Manufacturing precision
If mechanical adjustment mechanisms are used to align transmitter and receiver units, then alignment precision is improved, but device complexity increases
Solution Approach 1:
The invention replaces complex mechanical adjustment mechanisms with an electronic alignment system. Instead of using mechanical components to physically adjust the alignment between transmitter and receiver units, the system uses electronic control to measure alignment deviations and apply correction values, thereby achieving high alignment precision while reducing mechanical complexity.
Solution Approach 2:
The system achieves alignment adjustment by changing electronic parameters (correction values) rather than mechanical positions. The control unit calculates correction values based on measured alignment deviations and applies them electronically, allowing for precise alignment adjustment without requiring complex mechanical adjustment mechanisms.
3Manufacturing precision
If initial alignment during production is performed manually, then alignment precision is improved, but productivity decreases due to personnel expenditure
Solution Approach 1:
The system performs self-alignment during operation by automatically measuring its own alignment state through secondary light distribution analysis. The control unit autonomously calculates correction values and applies them without requiring manual intervention, thereby achieving high alignment precision while eliminating personnel expenditure and increasing productivity.
Solution Approach 2:
The invention replaces manual mechanical alignment procedures with an automated electronic alignment system. The system uses optical measurements and electronic calculations to achieve precise alignment automatically, eliminating the need for skilled personnel to perform manual adjustment during production, thus improving both precision and productivity.
4Reliability
If tolerance margins are increased to account for thermal expansion, then reliability is improved, but the detection range deteriorates due to over-design
Solution Approach 1:
The system performs preliminary measurements of the alignment state under different temperature conditions during operation. By detecting alignment deviations that occur with thermal expansion and comparing them with expected values, the system can calculate and apply correction values to maintain optimal alignment, thereby ensuring temperature stability without requiring tolerance margins that would reduce nominal performance.
Solution Approach 2:
The invention implements a feedback mechanism that continuously monitors alignment deviations caused by thermal expansion. The control unit receives measurement data, calculates correction values based on the detected deviations, and applies them in real-time, thereby maintaining reliable alignment across temperature variations without needing to over-design with tolerance margins that would compromise detection range.
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 alignment of LiDAR systems over their lifetime, maintaining performance despite temperature changes without the need for tolerance margins, and potentially eliminating the need for initial alignment during production.
Implementation Method 1
the transmitter unit emits primary light into the field of view, where it may be reflected, and then captured and detected as secondary light by the receiver unit
Implementation Method 2
a specific intensity distribution of the received secondary light is expected at or in the underlying detector arrangement for a particular viewing angle, solid angle, or angle of view
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
The present invention relates to a method for calibrating and/or adjusting a lidar system (1), in which method, in order to carry out a measurement-based comparison with respect to an underlying detector unit (20) which detects one-dimensionally or two-dimensionally, a distribution of secondary light (58) incident from the field of view (50, 50e) and imaged onto the detector unit (20) and a centre position and/or width of the distribution are recorded as position data and, in particular, are compared with an expected centre position and/or an expected distribution using presumed and/or expected position data.