Self-Driving Car Engineer Nanodegree Program
In this project I utilize a kalman filter to estimate the state of a moving object of interest with noisy lidar and radar measurements.
This project involves the Term 2 Simulator which can be downloaded here
Once the install for uWebSocketIO is complete, the main program can be built and run by doing the following from the project top directory.
Note that the programs includes src/FusionEKF.cpp, src/FusionEKF.h, kalman_filter.cpp, kalman_filter.h, tools.cpp, and tools.h
The program main.cpp has already been filled out by the project, except added some "cout", I dit not modify it.
main.cpp is using uWebSocketIO, which listen incoming message from the simulator, do some processing with EKL algorithms, then sent back to the simulator.
Bellow are the samples of the messages processed and transmitted on uWebSocketIO protocol.
["telemetry",{"sensor_measurement":"R 6.673922e+00 1.256145e-01 5.006870e+00 1477010444150000 6.561105e+00 7.925232e-01 5.141571e+00 4.883049e-01 9.468793e-02 1.633729e-01"}]
["telemetry",{"sensor_measurement":"L 6.782143e+00 7.140359e-01 1477010444200000 6.818081e+00 8.179882e-01 5.134523e+00 5.299897e-01 1.028566e-01 1.699593e-01"}]
42["estimate_marker",{"estimate_x":6.57681376720585,"estimate_y":0.744203439825412,"rmse_vx":1.53157158633846,"rmse_vy":0.899409160527193,"rmse_x":0.130520370666516,"rmse_y":0.0706979550892104}]
Final RMSE output as Bellow
42["estimate_marker",{"estimate_x":-7.23245983073131,"estimate_y":10.8959188865611,"rmse_vx":0.451266739945118,"rmse_vy":0.43993508070814,"rmse_x":0.097317778752952,"rmse_y":0.0854597168623381}]
The simulator screenshot as bellow:
The RMS output meets the requirements which is as bellow
px, py, vx, vy output coordinates must have an RMSE <= [.11, .11, 0.52, 0.52]
- mkdir build
- cd build
- cmake ..
- make
- ./ExtendedKF
I use XCode as develop environment, more details about how to setup is referred from bellow link here
