Adaptive LiDAR Scanning Guided by RGB Uncertainty Regions
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Solution Overview
Problem
Existing scanning technologies face challenges in efficiently and accurately detecting objects in complex construction environments with nontrivial geometries, such as occlusions and object stacking, due to high computational costs and redundant data capture, which hinder precise spatial analysis.
Innovation Solution
An adaptive scanning system that integrates RGB computer vision and LiDAR, using uncertainty scores based on Shannon information entropy to selectively enhance scanning of regions of interest, reducing unnecessary data capture and computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dense LiDAR scanning is performed across the entire scene to improve detection accuracy, then object detection precision improves, but computational cost and scan time increase significantly
Solution Approach 1:
The system applies different scanning densities to different regions of the scene based on their importance. High-density LiDAR scanning is concentrated on identified candidate objects and regions of interest, while low-density or no scanning is applied to background regions. This resolves the contradiction by maintaining high detection accuracy for critical objects while reducing overall scan time through selective localized scanning.
Solution Approach 2:
The scanning process is segmented into multiple phases: initial low-density global scanning to identify candidate objects, followed by targeted high-density scanning of specific ROIs. This segmentation allows the system to achieve high detection accuracy for important objects without requiring dense scanning of the entire scene, thus reducing total scan time while maintaining precision where needed.
2Measurement precision
If dense LiDAR scanning is performed across the entire scene to improve detection accuracy, then object detection precision improves, but computational cost increases
Solution Approach 1:
Computational resources are allocated locally to regions requiring detailed analysis. The system performs high-density scanning and complex processing only on candidate objects and ROIs identified by the initial low-density scan, rather than uniformly processing the entire scene. This reduces overall computational cost while maintaining high detection accuracy for critical objects.
Solution Approach 2:
The computational process is segmented into hierarchical stages: initial object candidate identification from low-density data, followed by focused processing of only those candidates. This segmentation eliminates the need to process entire dense scans of the full scene, significantly reducing computational cost while preserving detection accuracy for identified objects.
3Productivity
If low-density LiDAR scanning is used to reduce scan time and computational cost, then efficiency improves, but detection accuracy deteriorates
Solution Approach 1:
A low-density preliminary scan is performed first to identify candidate objects and regions of interest. Based on this preliminary information, the system then performs targeted high-density scanning only on identified ROIs. This preliminary action enables the system to achieve high detection accuracy for critical objects while maintaining overall scanning efficiency through selective follow-up scanning.
Solution Approach 2:
The low-density initial scan serves as an intermediary that guides subsequent high-density scanning. It provides the information needed to identify which regions require detailed scanning, acting as a mediator between efficiency requirements and accuracy requirements. This intermediary step enables the system to achieve both high productivity and high detection accuracy for important objects.
4Reliability
If comprehensive scene scanning is performed to capture all objects, then detection completeness improves, but data storage requirements increase
Solution Approach 1:
The system stores high-density LiDAR data only for identified candidate objects and regions of interest, rather than storing data from the entire scene. Low-density or no data is stored for background regions. This approach maintains detection completeness for important objects while significantly reducing overall data storage requirements through selective localized data retention.
Solution Approach 2:
The data storage process is segmented into global low-density storage for the entire scene and localized high-density storage for identified ROIs. This segmentation allows the system to maintain detection completeness by preserving detailed data where needed while reducing total storage requirements by using compressed or low-resolution data for less critical regions.
Data Source
AI summary
A method comprising generating, using a convolutional neural network model, one or more candidate objects based on one or more images of a scene, wherein the one or more images comprise one or more color depth images that are captured by a camera sensor; determining one or more uncertainty scores for the one or more candidate objects based on an information entropy function; and initiating, using a sparse light detection and ranging (LiDAR) sensor, scanning of one or more regions of interest (ROIs) that are determined based on the one or more uncertainty scores, wherein the scanning comprises (i) initiating capture of one or more enhancement frames for the one or more ROIs and (ii) generating one or more detected objects from the one or more ROIs based on the one or more enhancement frames.