Project Hub /Robotics & Autonomous Systems/RF / Wireless Controlled Robot/DIY 3D LiDAR Mapping: Convert a 2D LiDAR into a 3D Scanner

Intermediate

RASPBERRY PI

₹5,000 – ₹15,000

3 – 6 Hours

Partial Kit

DIY 3D LiDAR Mapping: Convert a 2D LiDAR into a 3D Scanner

This project demonstrates how to build an affordable 3D LiDAR mapping system by vertically moving a standard 2D LiDAR sensor while collecting scan data.

Originally published on ElectronicsForU

View original tutorial

Parts You'll Need

This project is sourced from another site, not written by us — this list is our best match of their components against what we stock, not a verified bill of materials. Always check the original blog (linked above) for the actual parts list before buying.

Raspberry Pi 3 - Model B+ 1.4Ghz 64-bit quad-core Processor with 1 GB Ram (Original) Rs. 5,400.00
A4988 driver Stepper Motor Driver Rs. 125.00
400 Points Mini Solderless Prototype PCB Breadboard High Quality Rs. 89.00
MB102 Adjustable Breadboard Power Supply Module (3.3V & 5V) for Arduino & Raspberry Pi Rs. 139.00
Jumper Wire 10 Pin Male To Male 20cm Rs. 45.00
Jumper Wire 10 Pin Male To Female 20cm Rs. 45.00

Selected: Rs. 0.00

We don't carry yet: Laser / LiDAR / ToF Sensor

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How It Works

This DIY 3D LiDAR mapping system operates by taking a standard 2D rotating LiDAR sensor, such as the YDLidar X2, and introducing a mechanical Z-axis movement using a stepper motor. While a typical 2D LiDAR only scans a single horizontal plane, mounting it on a motorized vertical stage allows the system to capture spatial data across multiple elevation angles. As the stepper motor incrementally adjusts the vertical orientation or height of the sensor, the LiDAR continuously captures planar distance measurements. A Raspberry Pi or laptop coordinates this mechanical movement while running Python scripts and the YDLidar-SDK to ingest the raw distance measurements. By synchronizing the precise position of the stepper motor with the rotational scan data from the LiDAR, software libraries like Open3D aggregate the individual 2D slices into a cohesive 3D point cloud. Finally, SLAM (Simultaneous Localization and Mapping) software processes these aggregated point clouds to construct a detailed volumetric representation of the surrounding environment.

Why Build This

Fabricate a low-cost alternative to commercial 3D LiDAR systems for autonomous robotics and advanced navigation research.

Gain hands-on experience integrating mechanical actuation with optical sensing to achieve volumetric spatial mapping.

Develop practical competencies in processing large point cloud datasets using modern software libraries like Open3D and SLAM algorithms.

Real-World Application

Robotics, mapping, surveying, ADAS, and construction site documentation.

Skills You'll Learn

Point cloud processing

stepper motor control

sensor data synchronization

3D spatial mapping

Python programming

SLAM integration

Safety Precautions

No specific precautions noted for this build — always follow general electronics safety practices.

Technology Tags

LIDAR

PYTHON

3D SCANNING

POINT CLOUD

SLAM

OPEN3D

YDLIDAR

ROBOTICS

Frequently Asked Questions

Can I use any 2D LiDAR sensor for this project?

The project is designed for the YDLidar X2 or a compatible 2D LiDAR that provides accessible software SDKs for data extraction.

Do I need a Raspberry Pi, or can I use a regular laptop?

You can use either a Raspberry Pi or a laptop; the choice depends on whether you want a tethered workstation setup or a fully mobile, onboard robotic mapping system.

Is the stepper motor strictly required?

The stepper motor is required if you want automated Z-axis movement to generate the 3D point cloud autonomously, though manual vertical adjustment is theoretically possible for static testing.

Gallery

This page is part of Kitkraft's Project Hub — a curated collection of electronics & robotics projects gathered from the maker community to help students and hobbyists discover and learn to build. Kitkraft does not claim authorship of the original project, and makes no representation as to the accuracy, completeness or safety of third-party source material. The summary and "How It Works" explanation above are AI-generated, rewritten with reference to the original tutorial — not a reproduction of it. Full credit and a direct link to the original source are provided above. If you're the original creator and have a concern about how your work is represented here, please contact us and we'll address it promptly.

Kitkraft Project Hub — curated from the maker community, credited at the source.

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