Event-Based Sensor Trajectory Processing With Cell-Wise 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 each cell, followed by intra-cell and inter-cell evaluations to determine trajectories efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If event data from all pixels is processed directly for trajectory determination, then measurement precision is improved, but device complexity increases dramatically
Solution Approach 1:
The pixel array is divided into multiple blocks, with each block processed independently to generate summary data. This segmentation reduces the computational complexity from O(N²) to O(N) by processing N pixels in parallel across M blocks, where M = N^(1/2) or similar partitioning scheme.
Solution Approach 2:
Summary data is extracted from each block containing only the essential information needed for trajectory determination (e.g., centroid position, event count, timestamp). This extraction reduces data volume while preserving the key characteristics needed for accurate trajectory reconstruction.
2Measurement precision
If high time resolution is used for point cloud movement, then measurement precision is improved, but productivity decreases due to increased processing operations
Solution Approach 1:
The pixel array is divided into multiple blocks that can be processed in parallel. This allows high time resolution processing to be achieved by distributing the computational load across multiple processing units, maintaining both precision and throughput.
Solution Approach 2:
Summary data is pre-computed for each block before trajectory determination. This preliminary aggregation of event data within blocks reduces the amount of data that needs to be processed at each time step, enabling faster processing while maintaining high temporal resolution.
3Device complexity
If data reduction processing is applied to events, then device complexity is reduced, but loss of information may occur
Solution Approach 1:
Essential information is extracted and preserved in summary data including spatial coordinates (centroid position), temporal information (timestamp, event count), and intensity metrics. This selective extraction maintains the critical features needed for trajectory determination while discarding redundant pixel-level details.
Solution Approach 2:
Different types of information are preserved with different levels of detail based on their importance for trajectory determination. Spatial position data is maintained with high precision, while less critical attributes are aggregated or simplified. This local quality approach ensures that the most important information is preserved while reducing overall data volume.
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 reduces data processing complexity and latency, facilitates parallel processing, and maintains locality of position data, enabling efficient trajectory determination even with multiple scanning beams.
Implementation Method 1
an event-based sensor (30) which comprises a pixel array (31) and is arranged to receive photons reflected or scattered by a region on an object (1) when illuminated by a scanning beam (21) of light
Data Source
AI summary
A stream of events from an event-based sensor is processed for trajectory determination. Each event comprises a pixel position in a pixel array of the event-based sensor, and a time stamp. 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.