Most affordable LiDAR units only see in 2D — a single flat slice of the world, with no sense of height. Commercial sensors that scan in full 3D cost thousands of dollars, putting real-time 3D mapping out of reach for students, hobbyists, and small robotics teams. We set out to close that gap using parts that together cost a fraction of a single commercial sensor.
The core idea: take a cheap 2D LiDAR and physically tilt it with a servo motor while continuously scanning. An IMU tracks the exact angle of every tilt, so each 2D scan line can be placed correctly in 3D space. Stack enough of these oriented slices together, and a full 3D point cloud emerges from a sensor that was never designed to produce one.
That point cloud is then fed into our own custom-built AI model, trained on the JackRabbot Dataset and Benchmark (JRDB), to detect and classify objects in the scanned environment — turning raw geometry into scene understanding. One application we're particularly drawn to is disaster response, where a low-cost 3D map of a space can help before anyone walks in — but the same pipeline applies to indoor mapping, robotics, and general 3D reconstruction.
Every component was chosen to keep the bill of materials low without sacrificing the orientation accuracy the 2D-to-3D conversion depends on.
2D LiDAR providing the raw range scans that get stacked into a 3D point cloud.
Runs point cloud generation and hands data off to the AI detection pipeline.
Drives the tilt sweep and synchronizes orientation data in real time.
Capacitive touch display for on-device control and live point cloud feedback.
The generated point cloud is passed through a modified version of the Residual Path Network with Efficient Attention (RPEA) detection model with a multi-branch detection head to specialize in occlusion patterns and distance bands in order to better detect humans in 3D space — no camera required, which also helps in low-light or visually cluttered environments.