Real-time LiDAR Room Detection at the Edge

A developer implemented real-time LiDAR room detection on an Arduino UNO Q using Edge Impulse, achieving high-accuracy environment classification without SLAM or computer vision. The system uses a 2D dToF LiDAR to create distance fingerprints, trains a neural network on 360-degree scans, and deploys the model for low-latency edge inference.

This project demonstrates an implementation of Edge AI for environment classification using a LiDAR without the computational overhead of traditional SLAM Simultaneous Localization and Mapping 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. The resulting model is deployed back to the Arduino UNO Q for real-time, low-latency inference. Hardware Specifications - Arduino UNO Q - LDRobot D500 LiDAR - USB-Hub Clone this application https://github.com/edgeimpulse/example-lidar-mapper-edge-impulse to test with your own device. Implementation Details The LDRobot D500 LiDAR sensor generates continuous 360° scans using dToF technology, providing distance measurements up to 12 meters. The Arduino UNO Q is running a Python-based application https://github.com/edgeimpulse/example-lidar-mapper-edge-impulse that collects data from the LiDAR and also can run inference using the Edge Impulse inference engine. Edge Impulse Studio serves as the orchestration layer for data ingestion, data segmentation, feature extraction, neural network classification training, quantization and model deployment. To prepare this data for a classification inference task, first capture the scans for a defined duration, such as 10 seconds and send them to Edge Impulse Studio using the Ingestion API https://docs.edgeimpulse.com/apis/ingestion . Then split the data into one-second windows. Each window represents a unique feature vector comprised of 360 dimensions, with one value representing the distance at each degree. Machine Learning Pipeline Edge Impulse The model architecture is engineered for a minimal computational footprint to ensure compatibility with edge hardware. In this case we are using Arduino UNO Q. We create an Impulse and utilize a Raw Data processing block, a Classification block mapping these 360-dimensional input vectors to specific space labels, such as corridor or room, and more. Deployment and Edge Inference Once the training is complete, the model is exported as an .eim Edge Impulse Model file. This file is deployed to the Arduino UNO Q's filesystem, where it is granted the necessary execution permissions chmod +X . During operation, the Python inference engine loads the model into memory, and as new LiDAR scans arrive, the ImpulseRunner performs rapid forward passes on the current feature vector. Results and Analysis Initial testing confirms high classification accuracy within controlled environments with very low latency. The primary advantage of this approach is the ability to distinguish between spaces based purely on geometric signatures instead of vision-based systems or similar. Next stepsSince this is one of my first robotics projects, feel free to share your feedback and comments in the comments below. Have you built something similar? Are there LiDAR use cases with edgeAI? Would you like to see more tutorials towards that direction? Read more javascript:void 0