MEMS LiDAR Galvanometer Field of View Abnormality Detection
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Solution Overview
Problem
The field of view (FOV) of a MEMS LiDAR's galvanometer can be affected by device performance degradation or harsh environments, leading to reduced detection range and potential security issues.
Innovation Solution
A method and device for detecting and determining the abnormality of the galvanometer's FOV by adjusting angles and acquiring echo data or feedback signal amplitudes to identify and correct any abnormalities, preventing the MEMS LiDAR from starting or continuing operation if the FOV is abnormal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the MEMS LiDAR operates in harsh environments or over time, then device performance may degrade and FOV may become abnormal, but continuing operation could cause security issues
Solution Approach 1:
The patent performs preliminary detection of the galvanometer's field of view by scanning a window at adjusted angles before allowing normal operation to begin. This preliminary action identifies potential FOV abnormalities early, preventing the system from operating in an unsafe state and thus resolving the contradiction between continuing operation and avoiding security issues.
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors the galvanometer's actual field of view and compares it against expected parameters. When abnormalities are detected, the system provides feedback to halt operation or adjust parameters, creating a closed-loop control that ensures safety while allowing normal operation to proceed under appropriate conditions.
2Reliability
If the system continuously monitors FOV during operation, then safety is improved, but device complexity increases
Solution Approach 1:
The patent makes the existing window scanning function serve multiple purposes: it not only performs the primary function of detecting objects in the environment but also simultaneously monitors the galvanometer's field of view for abnormalities. This multi-functionality approach allows the system to maintain safety monitoring without adding separate dedicated hardware or complex detection mechanisms.
Solution Approach 2:
The system uses its own existing scanning and detection capabilities to monitor its self-state (the galvanometer's FOV). By having the scanning system detect the window edges and calculate distances to determine FOV status, the system performs self-diagnosis without requiring external monitoring equipment or complex additional systems.
3Measurement precision
If the system adjusts galvanometer angles to scan window edges for detection, then FOV abnormality detection is improved, but detection time and processing complexity increase
Solution Approach 1:
The patent performs the window scanning and FOV detection as a preliminary action before allowing the main scanning operation to begin. By completing the angle adjustment and window edge detection in advance, the system ensures accurate FOV measurement without delaying the actual object scanning, thus resolving the contradiction between detection precision and time loss.
Solution Approach 2:
The patent scans only the window edges at specific adjusted angles rather than performing a complete comprehensive scan of the entire field of view. This partial action approach provides sufficient precision to detect FOV abnormalities while significantly reducing the time and processing complexity required compared to a full scan.
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
Ensures the reliability and safety of the MEMS LiDAR by preventing operation when the FOV is abnormal, improving safety and reliability by detecting and correcting issues in real-time.
Implementation Method 1
A Micro-Electro-Mechanical System (MEMS) LiDAR is suitable for large-scale application due to its small size and low cost. As the MEMS LiDAR is provided with a galvanometer, and a detection range of the MEMS LiDAR is closely related to a field of view (FOV) of the galvanometer.
Implementation Method 2
The system also includes a first LiDAR sensor with a first scanner to scan the first set of pulses of light along a first scan pattern, and a first receiver to detect scattered light from the first set of pulses of light.
Implementation Method 3
acquiring echo data from scanning a window at the adjusted angles of the galvanometer with respect to the X axis and the Y axis; and acquiring, from the echo data, distances between the MEMS LiDAR device and respective ones of an upper edge, a lower edge, a left edge and a right edge of the window
Data Source
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AI summary
The present application discloses an abnormal field of view recognition method and device, a storage medium and a MEMS LiDAR device, wherein the method comprises: respectively adjusting angles of a galvanometer with respect to an X axis and a Y axis when the MEMS LiDAR device is started, acquiring echo data from scanning a window at the adjusted angles of the galvanometer with respect to the X axis and the Y axis; acquiring, from the echo data, distances between the MEMS LiDAR device and respective ones of an upper edge, a lower edge, a left edge and a right edge of the window; and determining whether a field of view of the galvanometer is abnormal based on the acquired distances. According to the present application, abnormality of the field of view of the galvanometer can be detected when the MEMS LiDAR device is started, so that the security problem caused by the abnormal field of view of the galvanometer is avoided, and safety and reliability of the MEMS LiDAR device are improved.