LabLight AR System for Precise Object Tracking in Lab Procedures
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Solution Overview
Problem
Current AR/MR/XR devices lack the ability to accurately identify, localize, and track physical objects, especially small ones like screws or fingertips, due to spatial inaccuracy and limitations in object identification and pose determination, which hinders their utility in procedural guidance applications, particularly in laboratory settings where precision is crucial.
Innovation Solution
The LabLight system employs a combination of sensors and computing devices to provide interactive procedural guidance, using local processing to support object identification and pose determination through analytical geometry rather than deep neural networks, allowing for accurate spatial localization and tracking of physical objects, and generating virtual objects that adjust in real-time, reducing cognitive burden and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current AR/MR/XR devices are used for object identification and tracking, then the system can provide procedural guidance, but the spatial accuracy and object identification precision are insufficient
Solution Approach 1:
The patent replaces traditional camera-based visual recognition systems with sensor-based detection systems that use analytical geometry and mathematical models to determine object pose and position. This substitution enables more precise spatial measurement and object tracking by using sensors to detect physical quantities (such as position, orientation, movement) and converting them into actionable data for AR/MR/XR guidance, thereby resolving the contradiction between spatial accuracy and object identification reliability.
Solution Approach 2:
The patent changes the detection parameters from traditional image processing to sensor-based physical quantity measurement. By using sensors to directly measure position, orientation, and movement parameters, and by transforming these measurements into standardized coordinate systems, the system achieves higher spatial accuracy and more reliable object identification. This parameter transformation approach allows the system to overcome the limitations of conventional camera-based systems in terms of precision and reliability.
2Adaptability or versatility
If cloud-based processing is used for object identification, then computational resources are leveraged, but latency and security risks increase
Solution Approach 1:
The patent extracts the object identification and tracking functionality from cloud-based processing and implements it locally on the AR/MR/XR device. By using sensors and analytical geometry algorithms that run directly on the device, the system eliminates network dependency for these critical functions. This extraction enables real-time processing without latency while maintaining security by keeping sensitive data local, thus resolving the contradiction between computational capability and processing time.
Solution Approach 2:
The patent implements self-service processing where the AR/MR/XR device autonomously performs object identification, tracking, and spatial localization using its own sensors and computational resources. The device independently processes sensor data through analytical geometry without requiring cloud computation, enabling real-time responses and eliminating network latency. This self-service approach allows the system to maintain high adaptability while achieving immediate processing results, thereby resolving the time loss issue.
3Adaptability or versatility
If deep neural networks are used for object recognition, then identification capability is enhanced, but spatial localization accuracy decreases
Solution Approach 1:
The patent substitutes deep neural network-based recognition with sensor-based detection using analytical geometry. Instead of using image processing algorithms to infer object position, the system uses sensors to directly measure physical quantities and mathematically calculates precise spatial localization. This substitution maintains high object recognition capability through sensor data analysis while achieving superior spatial localization accuracy through direct measurement and mathematical computation, thereby resolving the contradiction between recognition capability and localization precision.
Solution Approach 2:
The patent changes the approach from data-driven deep learning to physics-based analytical geometry. By using sensors to measure physical parameters (position, orientation, movement) and applying mathematical models to calculate spatial relationships, the system achieves both accurate object recognition and precise spatial localization. This parameter change from indirect image analysis to direct physical measurement enables the system to overcome the trade-off between recognition capability and localization accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing augmented reality and quantify performance enhancement are disclosed. In one aspect, a method includes the actions accessing a protocol that specifies a procedure to be performed by an operator wearing an AR/MR/XR device. The actions further include generating instructions to assist the operator wearing the AR/MR/XR device in performing the procedure. The actions further include outputting the instructions. The actions further include receiving sensor data that reflects characteristics of an environment where the operator wearing the AR/MR/XR device is located. The actions further include determining whether the operator is performing the procedure correctly. The actions further include generating feedback to assist the operator wearing the AR/MR/XR device in determining whether to adjust actions being performed by the operator in performing the procedure. The actions further include outputting the feedback.