Dynamic Vision Sensor LIDAR Tracking Beam Control
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Solution Overview
Problem
Conventional LIDAR systems face trade-offs in speed, resolution, power consumption, and computation when measuring distance to targeted objects, and are limited by frame rate and lighting conditions, making them inefficient for tracking motion in high-speed and varying environments.
Innovation Solution
A vision-based LIDAR system utilizing a dynamic vision sensor (DVS) camera that asynchronously outputs pixel event data for brightness changes, allowing for faster motion tracking and reduced data processing, combined with a frame-based camera for image processing, and machine-learned models for object identification and beam control, enabling efficient tracking of objects in three dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional LIDAR systems use raster scan or full field of view illumination to generate depth maps, then complete area coverage is achieved, but processing time and computational load increase significantly
Solution Approach 1:
The patent extracts and processes only the relevant portion of the field of view containing the target object rather than processing the entire depth map. The system identifies target location from the depth map and directs the tracking beam only to that specific region, eliminating the need to process all pixels in the full field of view and significantly reducing computational load and processing time
Solution Approach 2:
The patent segments the field of view into regions of interest and processes them separately. Instead of treating the entire depth map as a single processing unit, the system divides the workspace into multiple regions and processes only those containing target objects, thereby reducing overall processing time while maintaining complete area coverage
2Loss of information
If conventional LIDAR systems process all pixels in the depth map, then complete scene information is captured, but data processing load and power consumption increase
Solution Approach 1:
The patent extracts only the necessary information from the depth map by identifying target object locations and processing only those specific regions. This selective processing approach maintains scene information completeness for target tracking while dramatically reducing the data processing load and associated power consumption compared to processing all pixels
3Device complexity
If conventional LIDAR systems use fixed frame rate cameras, then image capture is simplified, but motion tracking speed is limited
Solution Approach 1:
The patent employs a dynamic vision sensor that operates asynchronously without fixed frame rates. Each pixel independently detects brightness changes and outputs events as they occur, enabling the system to track fast-moving objects at higher speeds while maintaining operational simplicity through event-based processing rather than traditional frame-based capture
4Loss of information
If conventional LIDAR systems process all pixel data from the camera, then complete image information is available, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts only the relevant pixel events that contain target object information rather than processing all pixel data. By filtering and processing only the subset of events corresponding to the target region, the system maintains image information completeness for tracking purposes while significantly improving data processing efficiency and reducing computational resource requirements
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
The system achieves faster and more precise tracking of objects with reduced power consumption and data processing, operating effectively across a wider range of lighting conditions and speeds, while also reducing unnecessary data processing and improving object detection accuracy.
Implementation Method 1
The DVS camera measures brightness changes at each pixel independently and asynchronously outputs pixel event data that indicates locations of pixels with brightness change greater than a predetermined threshold
Implementation Method 2
determines distances to the one or more spots by detecting a portion of the tracking beam reflected from the one or more spots
Data Source
AI summary
A vision based light detection and ranging (LIDAR) system detects motion of a targeted object using a dynamic vision sensor to locate and track the targeted object. The dynamic vision sensor identifies activity events associated with the motion of the targeted object based on changes in brightness detected at pixels of the dynamic vision sensor. Based on the identified events, the vision based LIDAR system predicts a location of the targeted object and directs a tracking beam onto one or more spots on the targeted object and determines distances to the one or more spots to track the motion of the targeted object in three dimensions.