Laser Point Cloud Timestamp Segmentation for Autopilot Processing
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Solution Overview
Problem
Current methods for processing laser point cloud data with 64-bit precision timestamps are inefficient, leading to long processing times for CPUs/GPUs and high storage requirements, which compromise the real-time performance and stability of autopilot control systems.
Innovation Solution
The method involves splitting the 64-bit precision timestamp into a base timestamp and an offset timestamp, allowing for the storage and processing of compressed laser point cloud data, which includes only the offset timestamp, thereby improving processing speed and reducing storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 64-bit precision timestamps are used for laser point cloud data, then data synchronization precision is improved, but processing time increases and storage resources are consumed
Solution Approach 1:
The 64-bit timestamp is segmented into a base timestamp (common to all points in a frame) and offset timestamps (relative to the base). This segmentation allows the data receiver to process only the smaller offset values while reconstructing the full precision timestamp when needed, thereby improving processing speed while maintaining synchronization precision.
Solution Approach 2:
The base timestamp component is extracted and stored separately from the laser point cloud data. Only the offset timestamps remain in the compressed data structure, reducing the data volume that needs to be processed and stored, while the base timestamp can be added back when full precision is required for synchronization.
2Measurement precision
If 64-bit precision timestamps are used for laser point cloud data, then data synchronization precision is improved, but storage resources are consumed
Solution Approach 1:
The timestamp data is segmented into base and offset components, with only the offset portion stored in the compressed laser point cloud data. This reduces the storage requirement per timestamp from 64 bits to a smaller number of bits for the offset, while the base timestamp is stored once and reused for all points in the frame.
Solution Approach 2:
The base timestamp is extracted from each individual laser point's timestamp and stored as a separate frame-level attribute. This extraction eliminates redundant storage of the common base value across all points, significantly reducing total storage requirements while preserving full timestamp precision when needed.
3Reliability
If 64-bit precision timestamps are used for laser point cloud data, then synchronization accuracy is maintained, but CPU/GPU processing efficiency decreases
Solution Approach 1:
The timestamp is segmented into base and offset parts, allowing the processing system to work with the smaller offset values during routine operations. Full precision synchronization is maintained by adding the base timestamp back when needed, providing a balance between processing efficiency and synchronization accuracy.
Solution Approach 2:
The timestamp representation is changed from a single 64-bit value to a composite structure with a base timestamp and smaller offset values. This parameter change reduces the computational burden during processing while maintaining the ability to achieve full precision when synchronization accuracy is required.
Data Source
AI summary
A method and an apparatus for processing laser point cloud data includes obtaining laser point data to be used by a data receiver comprising an acquisition time; determining a timestamp for representing the acquisition time, and splitting the timestamp into a base timestamp and an offset timestamp; and storing the base timestamp and compressed laser point cloud data. Laser point cloud data output by a laser radar is compressed and comprises only offset timestamps corresponding to respective laser points. The base timestamp and the offset timestamp may be added to obtain the required synchronization precision timestamp, and the data is synchronized. The processing speed of a CPU or GPU for the laser point cloud data is improved while the timestamp precision reaches the precision required by synchronization of the laser point cloud data, and storage space is saved.