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.
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.
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CMakeLists.txt is the cmake file.
data folder contains test lidar and radar measurements.
Docs folder contains docments which describe the data.
src folder contains the source code.
mkdir build && cd build
cmake .. && make
cmake .. -G "Unix Makefiles" && make
./ExtendedKF ../data/obj_pose-laser-radar-synthetic-input.txt ./output.txt
./ExtendedKF ../data/sample-laser-radar-measurement-data-1.txt ./output.txt
In this demo, both LIDAR and RADAR measurements are used for object tracking.
In this demo, only LIDAR measurements are used for the object tracking.
In this demo, only RADAR measurements are used for the object tracking. are more noisy than the LIDAR measurements.
Note: the advantage of RADAR is that it can estimate the object speed directly by Doppler effect.
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.
|Direct velocity measure||no||yes||no|
|sense non-line of sight object||no||yes||no|
One comparison Figure from another aspect.
Q is the process covariance matrix.
All Kalman filters have the same three steps:
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.