Event-Based Sensor Trajectory Extraction via Grid-Cell Data Reduction
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Solution Overview
Problem
Existing systems face challenges in efficiently processing event data from event-based sensors for 3D positioning and object property determination due to high computational load and complexity in trajectory estimation, especially on resource-constrained systems.
Innovation Solution
A method involving computational partitioning of pixels into a grid of cells, with data reduction processing to generate summary data for trajectory determination, followed by intra-cell and inter-cell evaluations to reduce data complexity and enable parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If event data from all pixels is processed directly into trajectories, then trajectory accuracy is improved, but computational complexity increases dramatically
Solution Approach 1:
The patent divides the pixel array into multiple spatial zones or regions. Instead of processing all pixel events globally, the system processes events within each zone independently to generate local trajectory segments. These segments are then integrated to form complete trajectories. This segmentation reduces the computational complexity from O(N²) to O(N) where N is the number of pixels, while maintaining trajectory accuracy through proper zone boundary handling and segment integration.
2Measurement precision
If high time resolution is used for point cloud processing, then trajectory precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary processing of event data by organizing events into spatial zones and pre-computing local trajectory segments before final trajectory integration. This preliminary organization of data by spatial location and time allows the system to maintain high time resolution for accurate trajectory determination while reducing the overall processing time through efficient data structuring and parallel processing of independent zone segments.
3Measurement precision
If the number of pixels in the pixel array is increased, then image quality is improved, but computational load increases
Solution Approach 1:
The patent segments the large pixel array into multiple smaller spatial zones, allowing independent processing of events from each zone. This segmentation enables the system to handle high-resolution pixel arrays efficiently by processing smaller subsets of pixels in parallel, reducing the overall computational load from quadratic to linear complexity while maintaining the high image quality provided by the large number of pixels.
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 significantly reduces computational complexity and latency in trajectory determination, preserving locality and facilitating efficient processing of event data into trajectories, even with multiple scanning beams.
Implementation Method 1
an event-based sensor arranged to receive photons reflected or scattered by a region on an object when illuminated by a scanning light beam
Data Source
Figure 1A~2
Figure 3~4B
Figure 5A
AI summary
A stream of events from an event-based sensor (30, 40, 50) is processed (60) for trajectory determination. Each event comprises a pixel position in a pixel array (31, 41, 51) of the event-based sensor, and a time stamp, where the events originate from one or more light beams (21) scanned across a scene that is viewed by the sensor and includes an object (1). A grid of cells is defined for the pixel array, with each cell comprising a plurality of pixels. In the trajectory determination, incoming events are mapped to the grid of cells during a first time period, and summary data with a representative position for the pixel positions that fall within the respective cell during a first time period is generated. An intra-cell evaluation of the summary data generated for a second time period is performed to generate a cell trajectory of representative positions within the respective cell. An inter-cell evaluation of a plurality of cell trajectories generated for a plurality of cells for the second time period is performed to generate a grid trajectory. Accordingly, smaller trajectories are merged into larger trajectories by defining a computational partitioning of the pixels on the event-based sensor into a grid of cells and by performing a data reduction processing. Trajectory determination is facilitated when plural scanning beams are simultaneously moved within the scene that is viewed by the event-based sensor, provided that the scanning beams are separated in space at all times