IoT Camera Semantic Mapping for Real-Time Object Search
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Solution Overview
Problem
Existing electronic semantic maps struggle with real-time tracking of moving objects without GPS tags, leading to outdated maps and excessive resource consumption.
Innovation Solution
Utilize IoT device cameras with AI-enhanced semantic mapping to create 3D maps, allowing user-assisted labeling and periodic updates, and leverage sensor fusion for continuous map updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS tags are attached to each object for real-time tracking, then object tracking accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses visual copies (images) of objects captured by cameras as substitutes for physical GPS tags. Instead of attaching electronic tracking devices to objects, the system creates visual representations and tracks these copies through image recognition and processing, achieving tracking functionality without the complexity of physical tags on each object
Solution Approach 2:
The patent introduces cameras and image processing algorithms as intermediary elements between the objects and the tracking system. Rather than directly tracking objects with GPS tags, the system uses images as an intermediary medium to capture, identify, and track object positions and movements, simplifying the overall system architecture
2Measurement precision
If GPS tags are used for tracking, then real-time object location is improved, but energy consumption increases
Solution Approach 1:
The system tracks objects by processing visual copies (images) rather than using power-consuming electronic tags on each object. The energy-intensive tracking operations are centralized in the processing system rather than distributed across multiple battery-powered tags, significantly reducing overall energy consumption
Solution Approach 2:
The system performs tracking updates at periodic intervals by capturing images at scheduled times rather than continuously monitoring object positions. This periodic approach reduces energy consumption compared to continuous GPS tracking while still maintaining adequate real-time location information
3Device complexity
If no GPS tags are used, then device complexity is reduced, but semantic map becomes outdated quickly
Solution Approach 1:
The system maintains continuous tracking functionality by continuously capturing and processing images. The useful action of tracking is sustained over time through ongoing image acquisition and processing, ensuring the semantic map remains current without requiring periodic manual updates or complex tag management
Solution Approach 2:
The system implements feedback mechanisms where processed images continuously update the semantic map with current object positions and states. This feedback loop ensures the map remains synchronized with the real-world environment, maintaining reliability without GPS tags by constantly comparing detected objects against the stored semantic map
Data Source
AI summary
In one aspect, a first device includes a processor assembly and storage accessible to the processor assembly. The storage includes instructions executable by the processor assembly to access a semantic map, receive input from a first camera on an Internet of things (IoT) device, and update the semantic map based on the input.