Unscented Kalman Filter with LiDAR and Radar

🛰️ Harnessing the power of the Unscented Kalman Filter (UKF) for precision tracking of vehicles on a highway using a fusion of noisy LiDAR and Radar sensor measurements.

---

Project Details

This initiative aims to improve vehicular tracking by synergizing data from LiDAR, which primarily offers high-resolution spatial information, and Radar, known for its velocity detection capabilities even in challenging weather conditions.

Key Components:

1. Dynamic Scene Rendering: Utilizing highway.h, the simulation crafts a highway scenario with three auxiliary traffic vehicles and an ego car. The entire visualization pivots around the ego vehicle, establishing a relative coordinate system.

2. Algorithmic Insight: The Unscented Kalman Filter (UKF) is employed due to its robustness in handling non-linear process and measurement functions, inherent to vehicular movements and sensor fusion.

3. CTRV Model: Vehicles, unlike other entities, follow the Constant Turn Rate and Velocity (CTRV) model. This ensures a more accurate representation of real-world vehicular dynamics.

4. Data Visualization:

   • LiDAR Data: Represented by red spheres showcasing precise (x,y) positional detections.
   • Radar Data: Illustrated by purple lines, they exhibit the magnitude of velocity in the detected direction.
   • Predictions: Green spheres, hovering over cars, extrapolate future positions based on current state estimations.

5. Accuracy Metrics: Root Mean Squared Error (RMSE) values, calculated in real-time, offer insights into the (x,y) positional and (Vx, Vy) velocity accuracies of our UKF predictions.

---

Algorithm Performance and Tuning

Parameter tuning in UKF is imperative for optimal performance. Several parameters, including the state vector x_, covariance matrix P_, longitudinal acceleration noise std_a_, and yaw acceleration noise std_yawdd_, underwent rigorous testing. The chosen set consistently ensures that RMSE values remain within stipulated thresholds, underscoring the system's reliability.

---

Build Requirements

• cmake >= 3.5
  • All OSes: Installation Instructions
• make >= 4.1 (Linux, Mac), 3.81 (Windows)
  • Linux: make is installed by default on most Linux distros
• gcc/g++ >= 5.4
  • Linux: gcc/g++ is installed by default on most Linux distros
• PCL (Point Cloud Library) 1.2
  • Vital for processing and visualizing point cloud data from LiDAR.

---

Build & Execution Instructions

git clone https://github.com/ayushgoel24/Unscented-Kalman-Filter-with-LiDAR-and-Radar
cd Unscented-Kalman-Filter-with-LiDAR-and-Radar
mkdir build && cd build
cmake .. && make
./ukf_highway

---

Contributions and Feedback

We value community-driven enhancements. Whether it's algorithmic refinements, performance tuning, or any other improvements, please refer to our Contribution Guidelines and be part of this revolutionary project.

---

License

Distributed under the MIT License. Consult the LICENSE.md file for detailed permissions and restrictions.

---

Driven by the spirit of open-source collaboration and the pursuit of precision. Thank you for joining us on this journey! Safe and informed driving ahead! 🚗🛰️🌍