Object (e.g Pedestrian, vehicles) tracking by Extended Kalman Filter (EKF), with fused data from both lidar and radar sensors.

View the Project on GitHub JunshengFu/tracking-with-Extended-Kalman-Filter

Object Tracking with Sensor Fusion-based Extended Kalman Filter


Utilize sensor data from both LIDAR and RADAR measurements for object (e.g. pedestrian, vehicles, or other moving objects) tracking with the Extended Kalman Filter.

Demo: Object tracking with both LIDAR and RADAR measurements


In this demo, the blue car is the object to be tracked, but the tracked object can be any types, e.g. pedestrian, vehicles, or other moving objects. We continuously got both LIDAR (red circle) and RADAR (blue circle) measurements of the car’s location in the defined coordinate, but there might be noise and errors in the data. Also, we need to find a way to fuse the two types of sensor measurements to estimate the proper location of the tracked object.

Therefore, we use Extended Kalman Filter to compute the estimated location (green triangle) of the blue car. The estimated trajectory (green triangle) is compared with the ground true trajectory of the blue car, and the error is displayed in RMSE format in real time.

In autonomous driving case, the self-driving cars obtian both Lidar and radar sensors measurements of objects to be tracked, and then apply the Extended Kalman Filter to track the objects based on the two types of sensor data.

Code & Files

1. Dependencies & environment

2. My project files

(Note: the hyperlinks only works if you are on the homepage of this GitHub reop, and if you are viewing it in “” you can be redirected by clicking the View the Project on GitHub on the top)

3. Code Style

4. How to run the code

  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
    • On windows, you may need to run: cmake .. -G "Unix Makefiles" && make
  4. Run it by either of the following commands:
    • ./ExtendedKF ../data/obj_pose-laser-radar-synthetic-input.txt ./output.txt
    • ./ExtendedKF ../data/sample-laser-radar-measurement-data-1.txt ./output.txt

5. Release History

System details

1. Demos

Demo 1: Tracking with both LIDAR and RADAR measurements

In this demo, both LIDAR and RADAR measurements are used for object tracking.


Demo 2: Tracking with only LIDAR measurements

In this demo, only LIDAR measurements are used for the object tracking.


Demo 3:Tracking with only RADAR measurements

In this demo, only RADAR measurements are used for the object tracking. are more noisy than the LIDAR measurements.


From these three Demos, we could see that

Note: the advantage of RADAR is that it can estimate the object speed directly by Doppler effect.

2. How does LIDAR measurement look like

The LIDAR will produce 3D measurement px,py,pz. But for the case of driving on the road, we could simplify the pose of the tracked object as: px,py,and one rotation. In other words, we could only use px and px to indicate the position of the object, and one rotation to indicate the orientation of the object. But in real world where you have very steep road, you have to consider z axis as well. Also in application like airplane and drone, you definitely want to consider pz as well.

3. How does RADAR measurement look like

4. Comparison of LIDAR, RADAR and Camera

Sensor type LIDAR RADAR Camera
Resolution median low high
Direct velocity measure no yes no
All-weather bad good bad
Sensor size large small small
sense non-line of sight object no yes no


One comparison Figure from another aspect.

5. How does the Extended Kalman Filter Work

4. Extended Kalman Filter V.S. Kalman Filter

All Kalman filters have the same three steps:

  1. Initialization
  2. Prediction
  3. Update

A standard Kalman filter can only handle linear equations. Both the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF will be disuccsed in the next project) allow you to use non-linear equations; the difference between EKF and UKF is how they handle non-linear equations: Extended Kalman Filter uses the Jacobian matrix to linearize non-linear functions; Unscented Kalman Filter, on the other hand, does not need to linearize non-linear functions, insteadly, the unscented Kalman filter takes representative points from a Gaussian distribution.