Event-Based Sensor Spatio-Temporal Filtering for Stray Light Noise
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Solution Overview
Problem
Existing imaging systems using event-based sensors for 3D positioning are sensitive to stray light and noise, leading to difficulties in identifying relevant events and requiring computationally intensive filtering methods that are prone to false positives and negatives.
Innovation Solution
A spatio-temporal filtering technique is implemented, which involves initializing a data structure with pixel-specific time values and filtering events based on expected residence times and spatial patterns to isolate events related to a scanning light beam, reducing noise and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If event-based sensors are used for 3D positioning, then processing speed is improved, but sensitivity to stray light and noise increases
Solution Approach 1:
The patent applies preliminary action by predicting the light beam trajectory in advance using Kalman filter processing. The system calculates expected pixel locations and timestamps for light beam reflections before actual events occur, then uses these predictions to guide the filtering process. This allows the system to prepare filtering criteria ahead of time, improving processing speed while maintaining the ability to reject stray light and noise events that deviate from predictions.
Solution Approach 2:
The patent implements feedback by using predicted light beam trajectories to dynamically adjust which pixel events are accepted or rejected. The Kalman filter continuously updates predictions based on previous states and feeds this information back into the event filtering process. Events are compared against predictions, and only those consistent with the predicted trajectory are retained, creating a closed-loop system that maintains accuracy while operating at high speed.
2Object-affected harmful factors
If Kalman filter processing is used to predict light beam trajectory, then stray light suppression is improved, but computational load increases
Solution Approach 1:
The patent applies taking out by extracting only the essential predictive information needed for filtering rather than performing full Kalman filter processing on all possible events. The system extracts predicted pixel locations and timestamps from the Kalman filter and uses only this extracted information to guide event acceptance/rejection decisions. This reduces computational load by removing unnecessary processing steps while maintaining stray light suppression capability.
3Object-affected harmful factors
If event timing variations within timeslot are ignored, then noise reduction is improved, but temporal accuracy decreases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the timeslot duration based on the predicted light beam residence time at each pixel. Rather than using fixed timeslots that ignore temporal variations, the system modifies the timeslot parameter to match the actual expected event timing. This allows the system to reduce noise by grouping events appropriately while maintaining temporal accuracy by adapting the timeslot to the specific timing characteristics of each pixel location.
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 effectively filters out stray light and noise, enhancing the accuracy of event detection and reducing computational load, allowing for real-time processing and efficient memory usage.
Implementation Method 1
an event-based sensor arranged to receive photons reflected or scattered by a voxel on an object when illuminated by a scanning light beam
Data Source
AI summary
A computer-implemented method is performed to filter an incoming stream of events from an event-based sensor. Each event in the stream originates from an activated pixel in a pixel array of the event-based sensor and comprises an identifier of the pixel and an associated time stamp. The pixel is activated by photons from a scanning light beam. The method is based on the provision and updating of a data structure that spatially corresponds to the pixel array and has a data element for each pixel. The method updates (403) the data structure, based on the stream of events, so that each data element stores a time value that represents the most recent time stamp associated with the pixel that corresponds to the data element. The thus-updated data structure represents the events by both spatial and temporal data and enables the method to perform a spatio-temporal filtering (404) of the data structure to generate a filtered stream of events.