SF Course

This is work in progress for developing a series of modules for self studies in sensor fusion. Each module consists of a video lecture, slides, reading advice and recommended exercises. The videos are also available as a YouTube playlist for your convenience, sorted in the way we anticipate make them the most accessible.

Nr Subject Book Chapter Material Exercises
1 Introduction 1 slidesvideo (7:50)  
2 WLS 2-2.2 slidesvideo (9:15)  
3 The Fusion Formula 2.3 slides, video (6:41)  
4 Safe Fusion 2.3.5 slidesvideo (6:56)  
5 ML and CRLB 2.4-2.5 slidesvideo (9:34)  
6 NLS 3-3.2, 3.6 slidesvideo (12:23)  
7 Parameter Estimation using Nonlinear Transformations 3.4 (first part), 3.5 slidesvideo (12:26), m-files (for 7-9)  
8 Nonlinear Transformations Using Taylor Series Expansions 3.4.3 slidesvideo (6:53) 3.1ab, 3.2a-c,
9 Nonlinear Transformations Using Samples 3.4.2, 3.4.4 slides, video (9:49) 3.1c
10 Sensor networks: NLS 4-4.2 slides, video (12:59)  
11 Sensor networks: Tricks 4.3-4.6 slidesvideo (11:14)  
12 Detection theory 5 slides, video (11:21)  
13 Bayes versus Fisher 6 Intro slidesvideo (9:43)  
14 Bayes Filtering Recursion 6.3 slidesvideo (10:18)  
15 Filtering CRLB 6.5 slidesvideo (9:59)  
16 Continuous Time Motion Models 13-13.1,13.4 slidesvideo (8:09)  
17 Discretizing Motion Models 12 slidesvideo (11:50)  
18 Rotational Kinematics 13.2-13.3 slidesvideo (14:55)  
19 Wheel Speed Sensor Application 14.3 slidesvideo (6:48)  
20 Conditional Gaussian Distribution  7.1.3 slides, video (7:06)  
21 Kalman Filter 7-7.1 slides, video (15:39)  
22 Kalman Filter Properties 7.2-7.7 slides, video (9:25)  
23 Extended Kalman Filter (EKF) 8 (EKF related parts) slides, video (12:47)  
24 Unscented Kalman Filter (UKF) 8 (UKF related parts) slides, video (11:58)  
25 Application: shooter localization 16.1 slides, video (8:58)  
26 Application: Kalman filters 16.2 slides, video (18:09)  
27 Point Mass Filter 9.1-9.2 slides, video (14:39)  
28 Particle Filter 9.3 slides, video (17:23)  
29 Particle Filter Properties 9.4-9.6 slidesvideo (16:15)  
30 Marginalized Particle Filter 9.8 slides, video (17:58)  
31 Application: Particle filters 16.3 slides, video (19:16)  
32 Filter Banks 10 slides, video (21:10)  
33 Application: Kalman filter banks 14.2.4 slides, video (9:01)  
34 Simultaneous Localization and Mapping (SLAM): problem formulation 11-11.1 slides, video (13:25)  
35 Simultaneous Localization and Mapping (SLAM): EKF SLAM 11.2 slides, video (15:38)  
36 Simultaneous Localization and Mapping (SLAM): FastSLAM 11.3 slides, video (15:13)  
37 Application: RSS-SLAM   slides, video (14:30)