Raspberry Pi 5 Multi Sensor Fusion Navigation Kit — Accurate Localisation Using EKF on a Mobile Robot

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Build a differential-drive robot that fuses data from a LiDAR, IMU, and wheel encoders — running an Extended Kalman Filter on the Raspberry Pi 5 — to localise itself far more accurately than any single sensor alone. By combining complementary measurements, the robot overcomes drift, slip, and noise, giving you a real-world autonomous navigation platform ready for mapping, exploration, or competition tasks.

What You'll Build

You'll assemble a complete mobile robot on a chassis with encoder-equipped DC motors driven by the Cytron motor driver. After mounting the RPLidar A1, MPU9250 IMU, and connecting all peripherals to the Pi 5, you’ll run a Python-based EKF that reads odometry, inertial, and range data to estimate the robot’s pose in real time. The outcome is a wheeled platform that can navigate a room, hallway, or lab arena with robust localisation even when individual sensors momentarily degrade.

What You'll Learn

• Implement a full Extended Kalman Filter (EKF) for sensor fusion on a Raspberry Pi 5
• Calibrate and use wheel encoders to build a reliable odometry model
• Process 2D LiDAR scans for feature extraction and map-based localisation
• Synchronise IMU angular velocities with wheel odometry to compensate for drift

Kit Contents