FMCW LIDAR Point Cloud Frame Accumulation
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Solution Overview
Problem
FMCW 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 involves generating points based on scanning an environment, transforming these points into static frames by removing dynamic points corresponding to moving objects, combining static frames into an accumulated static frame with increased resolution, and loading this frame into a point cloud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If FMCW LIDAR systems use traditional scanning methods, then the system complexity remains manageable, but the measurement precision and resolution are insufficient for detecting small objects at far distances
Solution Approach 1:
The patent segments the point cloud data into multiple static frames by removing dynamic points corresponding to moving objects. Each static frame is processed independently and then accumulated, allowing high-resolution detection without requiring a single overly complex scanning system. The segmentation of scan lines into interlaced patterns further divides the scanning task into manageable segments.
Solution Approach 2:
The patent introduces temporal accumulation of static frames as an additional dimension to improve resolution. By accumulating multiple static frames over time and combining scan lines from different frames in an interlaced manner, the system achieves higher effective resolution without increasing the spatial or spectral dimensions of the scanning system.
2Measurement precision
If FMCW LIDAR systems accumulate multiple static frames to increase resolution, then the detection capability for small objects improves, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary removal of dynamic points from point cloud data before frame accumulation. By identifying and removing dynamic points early in the processing pipeline, the system reduces the computational burden of subsequent frame accumulation and merging operations, thereby reducing overall processing time while maintaining detection capability.
Solution Approach 2:
The patent applies partial action by selectively processing only static frames for accumulation rather than processing all point cloud data. By focusing computational resources on accumulating static frames with interlaced scan lines and leaving dynamic object processing separate, the system achieves efficient processing without unnecessary computational overhead.
3Reliability
If FMCW LIDAR systems remove dynamic points to create static frames, then ghosting effects are mitigated, but the quantity of available data for processing decreases
Solution Approach 1:
The patent extracts dynamic points from the point cloud data to create clean static frames. By removing dynamic points that cause ghosting effects during accumulation, the system improves reliability of static scene reconstruction. The extracted dynamic points are not discarded but processed separately and can be integrated back into the final point cloud, preserving data quantity while eliminating ghosting in the accumulated static representation.
Solution Approach 2:
The patent temporarily discards dynamic points during the static frame accumulation process to prevent ghosting, then recovers them by loading the most recent dynamic frame into the final point cloud. This selective discarding and recovering strategy maintains high reliability for static scene representation while preserving complete scene information including moving objects.
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 enhances the resolution of the point cloud, enabling better detection of small objects and features, and mitigates ghosting effects by removing dynamic points.
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.