Lidar Point Cloud Density Adjustment for Object Detection
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Solution Overview
Problem
Lidar systems face challenges in obtaining sufficient point cloud data for accurate object detection, classification, and characterization, particularly in regions of interest, due to insufficient density and resolution, which can hinder vehicle control systems' decision-making.
Innovation Solution
The method involves enhancing lidar point cloud data by increasing the density of measurements within a region of interest (ROI) or adjusting the field of view (FOV) to improve data density and resolution, allowing for better object detection and characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the lidar device collects point cloud data for the entire surrounding environment, then the coverage area is maximized, but the data density and resolution in specific regions of interest become insufficient
Solution Approach 1:
The patent applies local quality by differentiating data collection strategies between regions of interest and non-interest areas. The system dynamically adjusts point cloud measurement density based on spatial location, concentrating measurements in ROIs where objects are detected while maintaining lower density in other areas, thereby achieving both comprehensive coverage and high-resolution local detection
Solution Approach 2:
The system implements dynamic field of view adjustment by moving the FOV to track and focus on detected objects in real-time. This dynamic repositioning allows the lidar to adapt its coverage area and measurement density based on the detected environment, ensuring continuous high-resolution monitoring of relevant targets while maintaining overall situational awareness
2Measurement precision
If the lidar device increases the density of point cloud measurements within a region of interest, then the measurement precision for objects in that region improves, but the data collection time and processing load increase
Solution Approach 1:
The patent applies partial action by concentrating enhanced measurement density only in specific regions of interest where objects are detected, rather than uniformly increasing density across the entire field of view. This selective approach achieves high measurement precision for critical targets while avoiding the time penalty of comprehensive high-density scanning
Solution Approach 2:
The system performs preliminary detection to identify objects and determine regions of interest before intensifying measurement density in those areas. This preliminary identification allows the system to pre-position the FOV and prepare high-density measurement modes in advance, reducing the overall time required to achieve sufficient measurement precision
3Measurement precision
If the lidar device moves the field of view to follow an object, then the tracking precision improves, but the coverage area of the surrounding environment decreases
Solution Approach 1:
The patent segments the field of view into multiple operational zones: a focused high-resolution region for tracking detected objects and broader low-resolution regions for environmental monitoring. This segmentation allows the system to maintain precise object tracking while preserving awareness of the surrounding environment through coordinated scanning of multiple zones
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 enables improved object detection, classification, and characterization, enhancing the capability of vehicle control systems to make informed decisions by providing higher resolution and denser point cloud data within specific areas of interest.
Implementation Method 1
Lidar systems measure the attributes of their surrounding environments (e.g., shape of a target, contour of a target, distance to a target, etc.) by illuminating the environment with light (e.g., laser light) and measuring the reflected light with sensors
Implementation Method 2
In some cases, the range to a surface may be determined based on the time of flight of the channel's signal (e.g., the time elapsed from the transmitter's emission of the optical signal to the receiver's reception of the return signal reflected by the surface)
Implementation Method 3
In operation, each channel's transmitter can emit an optical signal (e.g., laser light) into the device's environment, and the channel's receiver can detect the portion of the signal that is reflected back to the channel's receiver by the surrounding environment
Data Source
AI summary
Methods and systems for collecting and using enhanced lidar point cloud data are provided. One example method includes using a lidar device to collect point cloud data for a surrounding environment. The method further includes determining that enhanced point cloud data should be obtained for a region of interest (ROI) in the environment. The method further includes, in response to the determination, collecting enhanced point cloud data for the object by at least one of: increasing a density of point cloud measurements within a region of interest (ROI) corresponding to the object, or moving a field of view (FOV) for the lidar device according to the ROI.