这是英国的一个Matlab Slam算法和路径规划作业代写

## Task 1 – Simultaneous Localisation and Mapping (50 marks)

In Task_1_SLAM.zip, the particle filter SLAM algorithm has been designed to navigate two

UAVs flying over the landmarks. In the current settings, both the range and bearing of the

landmarks are measured from the UAVs within the field of range.

### 1. Particle filter SLAM with bearing-only measurements:

Assuming that only the bearing measurements are available, modify the

measurement model, and derive the Jacobian matrix. Write their equations in the

report [2.5 marks]. Modify the codes accordingly to implement the SLAM algorithm

with bearing-only measurements. A suggestion is to modify fn_landmark_update.m,

Init_filter.m and/or Measurement.m, but you can change other scripts if needed.

Write the modified parts of the code in the report [5 marks]. Compare the

estimation error with the original results, and discuss how to improve the estimation

accuracy using bearing-only measurements [5 marks].

### 2. Particle filter SLAM with range-only measurements:

Assuming that only the range measurements are available, modify the measurement

model, and derive the Jacobian matrix. Write their equations in the report [2.5

marks]. Modify the codes accordingly to implement the SLAM algorithm with range

only measurements. A suggestion is to modify fn_landmark_update.m, Init_filter.m

and/or Measurement.m, but you can change other scripts if needed. Write the

modified parts of the code in the report [5 marks]. Compare the estimation error

with the original results, and discuss how to improve the estimation accuracy using

range-only measurements [5 marks].

### 3. Data association:

In Task_1_SLAM.zip, it is assumed that the assignments of the measurements to

landmarks are known, i.e. data association algorithm is not required. Assuming that

this is not known and data association algorithm should be implemented, discuss the

difficulties that may arise, comparing with the case of bearing-only or range-only

measurements [5 marks]. Also, describe possible resolutions to improve the data

association accuracy [5 marks].

### 4. EKF SLAM:

Another commonly used filter for SLAM is Extended Kalman Filter (EKF). Formulate

the EKF SLAM algorithm using both the range and bearing measurements, by

defining appropriate state and measurement models [2.5 marks], and by describing

the state/covariance predication and update steps [2.5 marks]. Implement the EKF

SLAM algorithm by modifying the given code [5 marks]. Compare the estimation

error with the original results, and discuss the differences between the particle filter

SLAM and the EKF SLAM algorithm [5 marks].