01
Define sensor sampling tasks
Assign acquisition responsibilities and timing requirements to each task.
Embedded · Advanced
Multi-sensor fusion with FreeRTOS
Schedule three sensors under FreeRTOS and fuse their readings into one Kalman-filtered estimate that is more stable than any single input.
Project build preview
Project blueprint
What you'll build
Multiple sensors become valuable when their readings are synchronized, filtered, and combined intentionally. This project uses FreeRTOS tasks for acquisition and a Kalman filter for a stable fused estimate.
It is a practical exercise in timing, task ownership, noisy measurements, and embedded system observability.
Included build referenceBuild reference — three sensors, task scheduling, and fused output
Before you start
Build requirements
01ESP32 development board
02Three compatible sensors
03FreeRTOS environment
04Basic C/C++ familiarity
From setup to working system
Build sequence
Follow the steps in order, verify each checkpoint, and finish with a working project you can explain and extend.
02
Synchronize and timestamp readings
Create a consistent measurement stream for values arriving at different rates.
03
Implement the fusion filter
Combine noisy inputs into a single estimate with predictable behavior.
04
Observe task timing
Measure queue pressure, task latency, and missed samples under load.
05
Validate the fused estimate
Compare the combined result with individual sensors and controlled motion.
Build the next checkpoint
Stop collecting tutorials. Ship a working build.
Use this project as a concrete piece of your embedded, IoT, or edge AI portfolio.
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