This lab consists of two parts: 1. A preparatory case study with a standard Kal...

Start with the runlocalization track.m which is the entrance function to your lab. This function reads two les determined by simoutle and maple input arguments which contain information about sensor readings and the map of the environment respectively, runs a loop for all the sensor readings and calls the ekf localize.m to perform one iteration of EKF localization on the readings and plots the estimation(red)/ground truth(green) and odometry(blue) information.-This lab consists of two parts: 1. A preparatory case study with a standard Kalman lter where you learn more about the behavior of the Kalman lter. Very little extra code is needed. 2. The main lab 1 problem in which you need to complete an implementation of an Extended Kalman lter based robot localization.

EKF_Peng
DataSets
associate.m
batch_associate.m
batch_update.m
calculate_odometry.m
displaySimOutput.m
drawLandmarkMap.m
ekf_localize.m
init.m
jacobian_observation_model.m
make_covariance_ellipses.m
observation_model.m
predict.m
runlocalization_track.m
update.m