FMCW LiDAR Static-Frame Accumulation for Small-Object Detection
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Solution Overview
Problem
FM CW LIDAR systems have lower resolution readings compared to imaging sensors, making it difficult to detect small objects or features at far distances.
Innovation Solution
The method generates static frames by removing dynamic points corresponding to moving objects and combines these frames to increase resolution, using sensor twist and scan pattern adjustments to enhance point cloud resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FM CW LIDAR systems use standard scanning methods, then the system operates with simpler processing, but the resolution is lower making it difficult to detect small objects or features at far distances
Solution Approach 1:
The patent segments the point cloud data into static and dynamic components by separating stationary points from moving objects. This segmentation allows the system to accumulate only static points across multiple frames, improving resolution without being hindered by dynamic objects that would blur the accumulated result
Solution Approach 2:
The system performs preliminary classification of points as static or dynamic before accumulation. By identifying and removing dynamic points in advance, the accumulation process can proceed efficiently with only static points, resolving the contradiction between improved resolution and processing complexity
2Measurement precision
If the system accumulates frames including dynamic objects, then more data is available for accumulation, but the resolution deteriorates due to moving objects
Solution Approach 1:
The patent extracts dynamic points from the point cloud data before accumulation. By removing these moving objects that would degrade resolution, the system accumulates only static points, thereby maintaining high resolution while still benefiting from multiple frame accumulation
Solution Approach 2:
The system dynamically classifies points as static or dynamic based on their motion characteristics. This dynamic separation allows selective accumulation of static points while excluding dynamic ones, resolving the contradiction between having sufficient data for accumulation and maintaining resolution
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 improves the detection of small objects and features by increasing the resolution of the point cloud, enabling better obstacle avoidance and scene analysis.
Implementation Method 1
The method receives returned optical beams in response to transmitting optical beams that are spaced non-uniformly based on a scan pattern
Implementation Method 2
The method retrieves a Doppler velocity of each of the points and compares the Doppler velocity of each one of the points with the sensor twist of the sensor
Data Source
AI summary
A method generates first points based on a first scan of an environment that includes one or more moving objects. The method transforms the first points into a first static frame, which includes removing one or more of the first points corresponding to the one or more moving objects. The method generates second points based on a second scan of the environment that includes the one or more moving objects. The method transforms the second points into a second static frame, which includes removing one or more of the second points corresponding to the one or more moving objects. The method combines the first static frame and the second static frame into an accumulated static frame, which has an increase in resolution compared with the first static frame. The method then loads the accumulated static frame into a point cloud.