LIDAR Timestamp Stream Processing for Memory Reduction
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Solution Overview
Problem
Conventional histogram-binning techniques in LIDAR systems require excessive memory and data processing due to the large number of bins needed for depth resolution, leading to inefficient data handling and increased power consumption.
Innovation Solution
The method determines the traveling time of a light pulse using a 'Find Frequent Items in a Data Stream' technique, which processes timestamp values to identify the most frequent time stamp, reducing memory requirements and enhancing data throughput and processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If histogram-binning technique is used to determine traveling time, then depth resolution is achieved, but memory size and data storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential information needed for depth measurement by identifying and storing only the most frequent timestamp value for each pixel, rather than storing all timestamp data in histogram bins. This extraction approach maintains depth resolution while dramatically reducing memory requirements from millions of bins to minimal storage per pixel.
Solution Approach 2:
Instead of the conventional approach of storing all timestamp data in histogram bins and then finding the peak, this patent inverts the process by directly identifying the most frequent timestamp value through stream processing, eliminating the need for large histogram storage structures while achieving the same depth measurement objective.
2Measurement precision
If sufficient number of bins are provided for depth resolution over entire range, then measurement precision is improved, but device complexity and data processing load increase
Solution Approach 1:
The patent extracts only the essential information needed for depth measurement by identifying and storing only the most frequent timestamp value for each pixel, rather than storing all timestamp data in histogram bins. This extraction approach maintains depth resolution while dramatically reducing memory requirements from millions of bins to minimal storage per pixel.
Solution Approach 2:
The patent changes the processing parameter from storing discrete histogram bin counts to maintaining a stream of timestamp values and identifying the most frequent value. This parameter change transforms the data structure from a large static histogram to a dynamic stream processing approach, reducing both memory and computational complexity.
3Loss of information
If timestamp data is stored for every photon-detection event, then complete data is retained, but power consumption and processing energy increase
Solution Approach 1:
The patent discards redundant timestamp data during stream processing, keeping only the most frequent timestamp value for each pixel. This selective discarding of redundant information maintains the essential depth measurement data while significantly reducing the energy required for data storage and processing.
Solution Approach 2:
The patent extracts only the essential information needed for depth measurement by identifying and storing only the most frequent timestamp value for each pixel, rather than storing all timestamp data in histogram bins. This extraction approach maintains depth resolution while dramatically reducing memory requirements from millions of bins to minimal storage per pixel.
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 provides accurate traveling time estimates comparable to conventional methods while reducing memory needs and power consumption, and allows for high-precision depth measurements without the need for extensive data storage.
Implementation Method 1
a series of laser light pulses that have been emitted from a light pulse source
Implementation Method 2
determining travel time for light pulses
Implementation Method 3
photon-detection events may be recorded... generate timestamps that are formed into a time histogram
Data Source
AI summary
A system and a method determines a traveling time for a light pulse between a light pulse source and a pixel of a light sensor array based on a “Find Frequent Items in a Data Steam” technique. In one embodiment, raw timestamp data output from a pixel as a data stream may be temporarily stored, processed twice and then discarded to provide an exact determination of a traveling time estimate. In another embodiment, the raw timestamp data is processed once and discarded to provide an approximate determination of a traveling time estimate. The traveling time estimate may be updated during processing and the most-frequently occurring timestamp is available when processing the data stream is complete. There is no need to keep the raw data in a memory, thereby reducing the memory requirement associated with determining the traveling time of a light pulse.