Build a Real-Time 3D Room Mapper with ESP32 & VL53L5CX
▼ Summary
– The ST VL53L5CX is a compact 8×8 grid sensor capable of distance measurements up to 4 meters.
– A demonstration showed this sensor can be used to create a 3D scan of a room when combined with an IMU for orientation data.
– An ESP32 microcontroller processes and combines the sensor and IMU data, streaming it as JSON to a computer.
– The sensor’s multizone array, 4-meter range, and 60 Hz sampling make it more suitable for 3D scanning than simpler models like the VL53L0X.
– The accompanying PC viewer software and ESP32 firmware are available on GitHub for implementation.
Creating a detailed three-dimensional map of a room in real time is now possible with a compact sensor and a microcontroller. The VL53L5CX from STMicroelectronics is a powerful time-of-flight sensor featuring an 8×8 multizone array, capable of measuring distances up to four meters at a rapid 60 Hz sampling rate. This makes it far more suitable for spatial scanning than simpler, single-point alternatives like the VL53L0X. In a practical demonstration, the sensor data is fused with orientation information from an inertial measurement unit (IMU) to construct a comprehensive 3D scan.
An ESP32 microcontroller serves as the central processing hub, handling the complex task of merging these two data streams. It applies necessary noise filtering to the raw sensor readings and utilizes plane-fitting algorithms to interpret the spatial data accurately. The processed information is then packaged and sent as a JSON stream to a connected computer for visualization.
The accompanying software, available in a public repository, includes both the firmware for the ESP32 and a Python-based viewer application for the PC. This viewer renders the incoming data, allowing users to see a dynamic, three-dimensional representation of the scanned environment. The project highlights the practical steps involved, from managing sensor calibration and orientation tracking to implementing the algorithms that transform raw distance measurements into a coherent spatial model.
(Source: Hackaday)
