Edge AI Room Detection Using 2D dToF LiDAR and Arduino UNO
By
Marc Pous
Summary
This project demonstrates Edge AI for environment classification using a 2D dToF LiDAR sensor connected to an Arduino UNO. Instead of using traditional SLAM or computer vision, it extracts 360-degree "distance fingerprints" from the environment, treats them as time-series feature vectors, and trains a neural network using Edge Impulse Studio. The trained model is deployed back to the Arduino UNO for real-time room detection at the edge, avoiding cloud dependency.
Source
bskyEdge AI Room Detection Using 2D dToF LiDAR and Arduino UNOhackster.ioKey quotes
· 3 pulledThis project demonstrates an implementation of Edge AI for environment classification using a LiDAR without the computational overhead of traditional SLAM or computer vision.
By utilizing a 2D dToF (Direct Time-of-Flight) LiDAR, we extract a 'distance fingerprint' from the surrounding environment.
These 360 degree distance profiles are treated as time-series feature vectors, which are then used to train a neural network with Edge Impulse Studio.
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