Lectures 2025

Lectures take place every Monday from 11:00-12:30 in KN:E-107.

Week Date Lecturer Block Topics Presentations
1 17.02.2025 KZ INTRO Introduction: course organization, prerequisites and problem definition 00_aro_outline.pdf
01_problem_definition.pdf
lecture_01_recording
2 24.02.2025 KZ PERCEPTION MODELING How to fuse almost anything: MAP, MLE, LS, 1D SLAM and factor graphs 00_1d_mle.pdf lecture_02_recording
lecture notes
3 03.03.2025 KZ PERCEPION MODELING Where the hell am I, and where is the stuff around me? SLAM in SE(2) with (i) measurement models of 2D/3D marker detectors, UWB, GPS/GNSS, odometry, and (ii) differential drive motion model 00_1d_mle.pdf
00_2d_mle.pdf
lecture_03_recording
4 10.03.2025 KZ PERCEPTION MODELING Can I build a map without markers? SLAM with lidar and camera and its efficient optimization on SE(2)/SE(3) manifolds (Absolute orientation, Camera localization/calibration, Levenberg-Marquardt) monoforce_2025.pdf
00_absolute_orientation.pdf
01_icp.pdf
lecture_04_recording
5 17.03.2025 KZ PERCEPTION OPTIMIZATION Optimization: Gauss_newton, Levenberg-Marquardt, Trust region methods, RANSAC 00_optimization_se2.pdf
01_ransac_simplified.pdf
lecture_05_recording
6 24.03.2025 KZ PERCEPTION OPTIMIZATION Filtering: Bayes and (Extended) Kalman filter 00_kf.pdf
01_ekf.pdf
lecture_06_recording
7 31.03.2025 KZ PERCEPTION OPTIMIZATION Beyond normal distributions: Non-parametric pdfs with Discrete Bayes filter and Particle Filter
00_bayes_filter.pdf
01_partical_filter.pdf
02_uncertainty_estimation.pdf
8 7.04.2025 VV PLANNING Exploration, world maps exploration.pdf Lecture_08_recording
9 14.04.2025 VV PLANNING Introduction to motion planning, Combinatorial motion planning motivation.pdf, basics.pdf Lecture_09_recording
10 21.04.2025 - —- Easter holidays —-
11 28.04.2025 VV PLANNING Combinatorial and Sampling-based motion planning I combinatorial.pdf, sampling1.pdf Lecture notes , Lecture_11_recording
12 05.05.2025 VV PLANNING Sampling-based motion planning II sampling2.pdf, Lecture_12_recording , Lecture notes
13 12.05.2025 VV PLANNING Sampling-based motion planning III sampling3.pdf, Lecture_13_recording
14 19.05.2025 VV PLANNING Data structures for motion planning sampling-datastructures.pdf, sampling-mppi.pdf Lecture_14_recording

Recordings of particular lectures are available here.

Playlist for planning videos (selected animations from the slides): Videos



OLD Lectures 2024

Lectures take place every Monday from 11:00-12:30 in KN:E-107.

Week Date Lecturer Topics Presentations
1 19.02.2024 KZ Introduction: course organization, prerequisites and problem definition 00_aro_outline.pdf
01_problem_definition.pdf
LOL lecture_01_recording
2 26.02.2024 KZ How to fuse almost anything: Localization and factor graphs 00_localization_mle.pdf
LOL lecture_02_recording
lecture_notes_02
3 04.03.2024 KZ Where the hell am I, and where is the stuff around me? SLAM in SE(2) with (i) measurement models of 2D/3D marker detectors, UWB, GPS/GNSS, odometry, and (ii) differential drive motion model 00_localization_se2.pdf
LOL lect_03_recording
4 11.03.2024 KZ How can I find myself without markers? SLAM with lidar and camera and its efficient optimization on SE(2)/SE(3) manifolds (Absolute orientation, Camera localization/calibration, Levenberg-Marquardt) 00_absolute_orientation.pdf
01_icp.pdf
LOLlect_04_recording
5 18.03.2024 KZ Do I really need to remember all that stuff forever? Kalman filter 00_kf.pdf
LOLlect_05_recording
lecture_notes_05_06
6 25.03.2024 KZ Maximum aposteriori estimate in real-time: Extended Kalman filter, Gauss_newton, Levenberg-Marquardt, Trust region methods 01_ekf.pdf
00_optimization_se2.pdf
LOLlec_06_recording
7 01.04.2024 - —- Easter holidays —-
8 8.04.2024 VV Exploration, introduction to motion planning 2024-exploration.pdf,
2024-planning-basics.pdf,
LOL Videos
9 15.04.2024 KZ Beyond normal distributions: Robust regression + RANSAC, Non-parametric pdfs with Bayes filter
Learning in robotics: Advanced optional lecture that assumes prior knowledge of deep-learning concepts.
00_bayes_filter.pdf
01_partical_filter.pdf
02_ransac_simplified.pdf
LOL lec_09_recording
lecture_notes_09
10 22.04.2024 VV Combinatorial motion planning 2024-planning-combinatorial.pdf Videos
11 29.05.2024 VV Sampling-based motion planning I 2024-planning-samplingi.pdf
12 06.05.2024 VV Sampling-based motion planning II 2024-planning-samplingii.pdf
13 13.05.2024 VV Sampling-based motion planning III 2024-planning-samplingiii.pdf, 2024-planning-mppi.pdf
14 20.05.2024 VV Data structures for motion planning 2024-planning-datastructures.pdf

Recordings of particular lectures are available here.

Lecturers

http://cmp.felk.cvut.cz/~zimmerk Karel Zimmermann (KZ) is the main lecturer and associate professor at the Czech Technical University in Prague. He worked as postdoctoral researcher with the Katholieke Universiteit Leuven (2008-2009) in the group of prof Luc van Gool. His current H-index is 13 (google-scholar) and he serves as a reviewer for major journals such as TPAMI or IJCV and conferences such as CVPR, ICCV, IROS. He received the best lecturer award in 2018, the best reviewer award at CVPR 2011 and the best PhD work award in 2008. His journal paper has been selected among 14 best research works representing Czech Technical University in the government evaluation process (RIV). Since 2010 he has been chair of Antonin Svoboda Award (http://svobodovacena.cz). He was also with the Technological Education Institute of Crete (2001), with the Technical University of Delft (2002), with the University of Surrey (2006). His current research interests include learnable methods for robotics.

http://mrs.felk.cvut.cz/people/vonasek Vojta Vonásek (VV) is the second lecturer and PostDoc researcher at the Department of Cybernetics. He spent one year at Karlsruhe Institute of Technology (KIT) at the institut IAR/IPR. He was a post-doc researcher at the Institut für Werkzeugmaschinen und Fabrikbetrieb, Technische Universität Berlin, Berlin, Germany within German Academic Exchange Service (DAAD) post-doc programme in 2017. He serves as the reviewer for robotic journals (Autonomous Robots, IEEE Robotics and Automation Letters, International Journal of Automation and Computing, Robotics and Autonomous Systems, ..) and many conferences. His research interests include path and motion planning, automatic learning of locomotion gaits of modular robots and application of motion planning techniques in computational biochemistry.

courses/aro/lectures/start.txt · Last modified: 2025/05/25 07:48 by vonasvoj