This lab consists of two parts:
1. A preparatory case study with a standard Kal...
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标 签：
Windows开发
matlab
资 源 简 介
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.
文 件 列 表
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
