Utilize sensor data from both LIDAR and RADAR measurements for object (e.g. pedestrian, vehicles, or other moving objects) tracking with the Unscented Kalman Filter.
All Kalman filters have the same mains steps: 1. Initialization, 2. Prediction, 3. Update. A Standard Kalman Filter (KF) can only handle linear equations. Both the Extended Kalman Filter (EKF) and the Unscented Kalman Filter 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.
Table 1: Accuracy comparison in RMSE by using EKF and UKF with both lidar and radar measurements. The lidar and radar measurements are included in the txt file under the data folder.
Table 2: Accuracy comparison in RMSE by UKF with different sensor measurements.
|state||lidar and radar||only lidar||only radar|
Conclusions from aboves:
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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