Point Cloud Sliding Window Caching for LiDAR Scan Reordering
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Solution Overview
Problem
The challenge in LiDAR systems is the inaccurate retrieval of point cloud data due to the spiral scanning trajectory of MEMS galvanometers, which causes points at upper spatial positions to be detected later than those at lower positions, necessitating reordering and increasing chip area and power consumption.
Innovation Solution
A point cloud data processing method that reads current sliding window data from a cache, determines target point data based on tag information, and updates the data for accurate computation, reducing chip area by optimizing the sliding window processing flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If point cloud data is reordered to correct the spiral scanning trajectory detection sequence, then data retrieval accuracy is improved, but chip area and processing complexity increase
Solution Approach 1:
The point cloud data is divided into multiple sliding windows, each processed independently. This segmentation allows the system to handle data in manageable chunks without requiring complete reordering of the entire point cloud, thereby reducing the computational burden and chip area while maintaining retrieval accuracy within each window.
Solution Approach 2:
The patent pre-processes point cloud data by organizing it into sliding windows with associated tag information before main processing. This preliminary organization enables efficient data retrieval during computation without requiring complex real-time reordering operations, thus improving accuracy while minimizing chip area requirements.
2Reliability
If complete point cloud data is cached to ensure processing accuracy, then computational accuracy is improved, but chip area increases
Solution Approach 1:
Instead of caching complete point cloud data, the patent caches only relevant sliding window data segments. Each cache entry contains point cloud data and tag information for a specific spatial region, enabling accurate processing while significantly reducing the total cache memory required compared to storing all point cloud data.
Solution Approach 2:
The patent implements selective caching where only locally relevant data within sliding windows is stored in cache memory, rather than uniformly caching all data. This local quality approach ensures computational accuracy for processed regions while minimizing overall chip area dedicated to caching.
Data Source
AI summary
The present application provides a point cloud data processing method, an apparatus, and a point cloud data processing circuit. The point cloud data processing method includes: when processing current point data by an i-th level algorithm, reading current sliding window data centered on the current point data, the current sliding window data including at least two cached data, each cached data being stored in a corresponding cache location, each cached data comprising point data and tag information identifying row number information of the point data, where i is an integer greater than 1; determining target point data based on the tag information, and determining corresponding cached data based on the target point data; and updating the current sliding window data based on the cached data that has been determined, and performing the i-th level algorithm processing on the current sliding window data.