Download Mobile Robotics by Paul Michael Newman, This set of lectures is regarding navigating mobile platforms or robots. this is a large topic and in eight lectures we can only hope to undertake a brief survey. The course is an extension of the B4 estimation course covering topics like linear and non-linear Kalman Filtering. The estimation part of the lectures is applicable to several areas of engineering not simply mobile robotics. but I hope that couching the material in a robotics scenario will build the material compelling and interesting to you.


1 Introduction and Motivation

2 Introduction to Path Planning and Obstacle Avoidance
2.1 Holonomicity
2.2 Configuration Space
2.3 The Minkowski-Sum
2.4 Voronoi Methods
2.5 Bug Methods
2.6 Potential Methods

3 Estimation - A Quick Revision
3.1 Introduction
3.2 What is Estimation?
3.2.1 Defining the problem
3.3 Maximum Likelihood Estimation
3.4 Maximum A-Posteriori - Estimation
3.5 Minimum Mean Squared Error Estimation
3.6 Recursive Bayesian Estimation

4 Least Squares Estimation
4.1 Motivation
4.1.1 A Geometric Solution
4.1.2 LSQ Via Minimisation
4.2 Weighted Least Squares
4.2.1 Non-linear Least Squares
4.2.2 Long Baseline Navigation - an Example

5 Kalman Filtering -Theory, Motivation and Application
5.1 The Linear Kalman Filter
5.1.1 Incorporating Plant Models - Prediction
5.1.2 Joining Prediction to Updates
5.1.3 Discussion
5.2 Using Estimation Theory in Mobile Robotics
5.2.1 A Linear Navigation Problem - “Mars Lander”
5.2.2 Simulation Model
5.3 Incorporating Non-Linear Models - The Extended Kalman Filter
5.3.1 Non-linear Prediction
5.3.2 Non-linear Observation Model
5.3.3 The Extended Kalman Filter Equations

6 Vehicle Models and Odometry
6.1 Velocity Steer Model
6.2 Evolution of Uncertainty
6.3 Using Dead-Reckoned Odometry Measurements
6.3.1 Composition of Transformations

7 Feature Based Mapping and Localisation
7.1 Introduction
7.2 Features and Maps
7.3 Observations
7.4 A Probabilistic Framework
7.4.1 Probabilistic Localisation
7.4.2 Probabilistic Mapping
7.5 Feature Based Estimation for Mapping and Localising
7.5.1 Feature Based Localisation
7.5.2 Feature Based Mapping
7.6 Simultaneous Localisation and Mapping - SLAM
7.6.1 The role of Correlations

8 Multi-modal and other Methods
8.1 Montecarlo Methods - Particle Filters
8.2 Grid Based Mapping

9 In Conclusion

10 Miscellaneous Matters
10.1 Drawing Covariance Ellipses
10.2 Drawing High Dimensional Gaussians

11 Example Code
11.1 Matlab Code For Mars Lander Example
11.2 Matlab Code For Ackerman Model Example
11.3 Matlab Code For EKF Localisation Example
11.4 Matlab Code For EKF Mapping Example
11.5 Matlab Code For EKF SLAM Example
11.6 Matlab Code For Particle Filter Example