Indoor Survey Data Collection with Outlier Rectification
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Solution Overview
Problem
Indoor survey data collection for localization systems is plagued by human errors due to non-adherence to protocols, technical literacy issues, and challenges in navigating unfamiliar locations, which are costly and inefficient, especially when requiring high-skilled personnel for large-scale data collection across multiple sites.
Innovation Solution
A data collection system that generates reference points based on site maps and accuracy requirements, uses a data collecting agent to collect data, detects and eliminates outliers, and rectifies data using feedback and machine learning methods to enhance data accuracy and quality in an online fashion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection by personnel is used, then data can be collected at reference points, but human errors occur due to non-adherence to protocols, technical literacy issues, and navigation challenges in unfamiliar locations
Solution Approach 1:
The system enables self-service data collection through autonomous robots that navigate and collect data without human intervention. The robots independently follow protocols, navigate to reference points using site maps and feedback, and collect survey data, eliminating human error sources while maintaining data collection functionality
Solution Approach 2:
The patent replaces the mechanical human-operated data collection system with an automated robotic system. The mechanical navigation and data collection tasks previously performed by human personnel are substituted with robotic agents that use sensors, processors, and automated navigation algorithms to perform the same functions with higher precision and reliability
2Measurement precision
If high-skilled personnel are deployed for data collection, then data quality improves, but costs increase and scalability to multiple sites becomes inefficient
Solution Approach 1:
The robotic system performs self-service data collection autonomously without requiring high-skilled personnel at each site. The robots independently execute survey protocols, navigate using site maps, and collect data, enabling scalable deployment across multiple sites without proportionally increasing skilled labor requirements
Solution Approach 2:
The system uses standardized robotic agents that can be deployed and copied across multiple sites. Rather than requiring unique high-skilled personnel at each location, the same robotic data collection platform can be replicated and deployed site-wide, improving productivity and scalability while maintaining consistent data quality standards
3Reliability
If automated data collection is implemented, then human error is reduced, but system complexity increases requiring processors, algorithms, and feedback mechanisms
Solution Approach 1:
The system implements feedback mechanisms where the robotic data collection agent communicates with a central processor, providing real-time status information and receiving guidance. This feedback loop enables automated outlier detection, navigation adjustments, and data quality verification, achieving high reliability through controlled complexity rather than unmanaged simplicity
Solution Approach 2:
The patent introduces an intermediary processing system that mediates between the robotic data collection agents and the final data output. This intermediary layer handles complex tasks such as outlier detection, data validation, and coordination, isolating the complexity from the field operations and enabling reliable automated data collection without requiring excessive complexity at the robotic agent level
Data Source
AI summary
In an approach for an indoor survey data collection, a processor generates reference points based on a site map and an accuracy requirement. A processor collects data at each reference point through a data collecting agent. A processor detects an outlier at the reference points using a feedback from the data collecting agent during the data collection and a database. A processor eliminates the detected outlier and rectifies the data.