Point Cloud Map Generation Using Quality Score Feedback
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Solution Overview
Problem
Conventional techniques for aligning and registering point clouds in real-world environments lack accuracy, leading to unreliable data and inefficient use of computing resources, which can be critical in applications like autonomous vehicles.
Innovation Solution
A system comprising data acquisition devices and a server arrangement with a receiving module, registration module, and data processing module to acquire, register, and generate point cloud maps, using a quality score to ensure alignment and reduce manual intervention, thereby enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional alignment algorithms (e.g., ICP) are used to register point clouds, then the registration process can be automated, but the accuracy and reliability of the registered data deteriorates
Solution Approach 1:
The patent implements a feedback mechanism by calculating a quality score for each registered point cloud pair and using this score to determine whether to accept or reject the registration. The system continuously monitors registration quality and adjusts the process accordingly, ensuring that only high-quality registrations are used to build the final point cloud map.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating quality scores for potential point cloud pairs before final registration. It identifies and selects only those pairs with quality scores above a threshold, preparing the data in advance to ensure high-quality registration results without manual intervention.
2Reliability
If multiple point clouds are registered to capture complete environment, then the coverage and completeness of the map improves, but the computational resources and time required increases
Solution Approach 1:
The patent applies partial action by selectively processing only those point cloud pairs that meet a quality threshold. Instead of registering all possible pairs, it identifies and processes only the necessary subset that contributes to complete environment coverage, reducing computational overhead while maintaining map completeness.
Solution Approach 2:
The patent segments the point cloud registration process into discrete quality-assessed pairs. Each pair is independently evaluated and registered separately based on quality metrics, allowing the system to manage large-scale environment capture through modular, efficient processing units rather than attempting to register all data simultaneously.
3Measurement precision
If manual intervention is used to align point clouds, then the accuracy of registration improves, but the time required and operational complexity increases
Solution Approach 1:
The patent enables self-service by implementing an automated quality assessment system that evaluates point cloud pairs and determines their suitability for registration without human intervention. The system automatically calculates quality scores, compares them against thresholds, and makes registration decisions, eliminating the need for manual review while maintaining high accuracy standards.
4Productivity
If low-quality registered data is accepted to reduce processing requirements, then the computational resources are conserved, but the reliability of the generated map deteriorates
Solution Approach 1:
The patent uses feedback control by continuously monitoring the quality score of registered point cloud pairs and using this information to determine whether to accept or reject each registration. This feedback mechanism ensures that only reliable data is incorporated into the final map, preventing the degradation of map reliability even when processing resources are constrained.
Data Source
AI summary
A system for generating a point cloud map of one or more objects in real-world environment. The system including data acquisition device for acquiring plurality of data points representing objects, wherein data acquisition device is configured to acquire first set of data points for objects from first position to generate first point cloud, and acquire second set of data points for objects from second position to generate second point cloud, a server arrangement including a receiving module configured to receive first point cloud and second point cloud, registration module to register received first point cloud and received second point cloud to generate a point cloud pair which is aligned and data processing module to determine quality score for generated point cloud pair, compare determined quality score with predefined threshold value, and generate point cloud map if determined quality score is less than a predefined threshold value.