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Robot Mapping - WS 2012/13

Robot Mapping

What is this lecture about?

The problem of learning maps is an important problem in mobile robotics. Models of the environment are needed for a series of applications such as transportation, cleaning, rescue, and various other service robotic tasks. Learning maps requires solutions to two tasks, namely basic mapping and localization. Basic mapping is the problem of integrating the information gathered with the robot's sensors into a given representation. It can intuitively be described by the question ``What does the world look like?'' Central aspects in mapping are the representation of the environment and the interpretation of sensor data. In contrast to this, localization is the problem of estimating the pose of the robot relative to a map. In other words, the robot has to answer the question ``Where am I?'' These two tasks cannot be solved independently of each other. Solving both problems jointly is often referred to as the simultaneous localization and mapping (SLAM) problem. There are several variants of the SLAM problem including passive and active approaches, topological and metric SLAM, feature-based vs. volumetric approaches, and may others.

The lecture will cover different topics and techniques in the context of environment modeling with mobile robots. We will cover techniques such as SLAM with the family of Kalman filters, information filters, particle filters. We will furthermore investigate graph-based approaches, least-squares error minimization, techniques for place recognition and appearance-based mapping, data association as well as information-driven approaches for observation processing. The exercises and homework assignments will also cover practical hands-on experience with mapping techniques, as basic implementations will be part of the homework assignments.

Organization

  • Lecturer: Cyrill Stachniss
  • Tutors: Rainer Kuemmerle and Nichola Abdo
  • Lecture: Mon, 10-12 in building 101, SR 01-018
  • Exercise: Wed, 14-16 in building 101, SR 01-018. Exercises start on Wed 31.10.12
  • Online Questions and Answers: We will try to answer your questions as fast as possible (not available anymore!).
  • The lecture recordings are also available via YouTube.
  • Recordings, slides, homework assignments, and additional material will be available via this website.
  • Teaching is done in English.
  • The lecture is part of the area Cognitive Technical Systems in the CS master program.

Schedule

Date Topic Slides Recordings Homework Assignment Relevant Literature
22.10. Introduction to Robot Mapping 01.pdf
01-4up.pdf
MP4 795MB
MP4 441MB
Youtube
--- Springer Handbook on Robotics Chapter 37.1 + 37.2
29.10. Bayes Filter and Related Models (Summary of Introduction to Mobile Robotics) 02.pdf
02-4up.pdf
MP4 499MB
MP4 291MB
Youtube
Sheet1
Octave-Code
Octave Help
Introduction to Mobile Robotics lecture
Probabilistic Robotics Book, Chapters 2, 5, 6
29.10. Kalman Filter and Extended Kalman Filter (EKF) 03.pdf
03-4up.pdf
MP4 429MB
MP4 250MB
Youtube
--- Probabilistic Robotics Book, Chapter 3.1-3.3
Manipulating the Multivariate Gaussian Density
Kalman Filter Tutorial by Welch and Bishop
05.11. EKF SLAM 04.pdf
04-4up.pdf
MP4 1GB
MP4 591MB
Youtube
Sheet2
Octave-Code
Probabilistic Robotics Book, Chapter 10
12.11. Unscented Kalman Filter (UKF) 05.pdf
05-4up.pdf
MP4 580MB
MP4 336MB
Youtube
Sheet3
Octave-Code
Probabilistic Robotics Book, Chapter 3.4
12.11. Extended Information Filter (EIF) 06.pdf
06-4up.pdf
MP4 310GB
MP4 180MB
Youtube
--- Probabilistic Robotics Book, Chapter 3.5
21.11. Sparse EIF SLAM - Part 1 07.pdf
07-4up.pdf
MP4 800MB
MP4 465MB
Youtube
Sheet4
Octave-Code
Probabilistic Robotics Book, Chapter 12.1-12.7
SEIF Paper
Computing inv(Gt)
26.11. Sparse EIF SLAM - Part 2 see above MP4 580MB
MP4 308MB
Youtube
--- Probabilistic Robotics Book, Chapter 12.1-12.7
SEIF Paper
SEIF: Insights on Sparsification
26.11. Summary on the Kalman Filter and its Friends for SLAM 08.pdf
08-4up.pdf
MP4 140MB
MP4 81MB
Youtube
--- ---
26.11. Short Introduction to the Particle Filter and PF Localization 09.pdf
09-4up.pdf
MP4 302MB
MP4 175MB
Youtube
Sheet5
Octave-Code
Probabilistic Robotics Book, Chapter 8.3
3.12. FastSLAM - Part 1 10.pdf
10-4up.pdf
MP4 942MB
MP4 548MB
Youtube
Sheet6
Octave-Code
Probabilistic Robotics Book, Chapter 13.1-13.3 + 13.8
FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem
Simultaneous Localization and Mapping with Unknown Data Association Using FastSLAM
10.12. FastSLAM - Part 2 see above MP4 562MB
MP4 320MB
Youtube
--- Probabilistic Robotics Book, Chapter 13.1-13.3 + 13.8
FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem
Simultaneous Localization and Mapping with Unknown Data Association Using FastSLAM
10.12. Grid Maps 11.pdf
11-4up.pdf
MP4 482MB
MP4 280MB
Youtube
Sheet7
Octave-Code
Probabilistic Robotics Book, Chapter 4.2, 9.1-9.2
17.12. Scan-Matching in 5-10 Minutes 12.pdf
12-4up.pdf
MP4 160MB
MP4 94MB
Youtube
--- A method for Registration of 3-D Shapes
Real-Time Correlative Scan Matching
17.12. Grid-based SLAM with Rao-Blackwellized Particle Filters 13.pdf
13-4up.pdf
MP4 743MB
MP4 432MB
Youtube
--- Improved Techniques for Grid Mapping with Rao- Blackwellized Particle Filters
Analyzing Gaussian Proposal Distributions for Mapping with Rao-Blackwellized Particle Filters
Probabilistic Robotics Book, Chapter 13.10
07.01. Least-Squares 14.pdf
14-4up.pdf
MP4 889MB
MP4 517MB
Youtube
Sheet8
Octave-Code
Methods for Non-Linear Least Squares Probelms
Wiki:Gauss-Newton
Probabilistic Robotics Book, Chapter 11.4
14.01. Least-Squares Approach to SLAM 15.pdf
15-4up.pdf
MP4 907MB
MP4 528MB
Youtube
Sheet9
Octave-Code
A Tutorial on Graph-based SLAM
Probabilistic Robotics Book, Chapter 11 Methods for Non-Linear Least Squares Probelms
21.01. Least-Squares Approach to SLAM - Additional Remarks 15b.pdf
15b-4up.pdf
MP4 186MB
MP4 108MB
Youtube
--- A Tutorial on Graph-based SLAM
Probabilistic Robotics Book, Chapter 11
21.01. Hierarchical Pose-Graphs for Online Mapping 16.pdf
16-4up.pdf
MP4 378MB
MP4 220MB
Youtube
--- Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping
21.01. Graph-Based SLAM with Landmarks 17.pdf
17-4up.pdf
MP4 301MB
MP4 175MB
Youtube
--- Probabilistic Robotics Book, Chapter 11
Methods for Non-Linear Least Squares Probelms
28.01. Max-Mixture and Robust Least Squares for SLAM 18.pdf
18-4up.pdf
MP4 470MB
MP4 272MB
YouTube
--- Inference on Networks of Mixtures for Robust Robot Mapping
Methods for Non-Linear Least Squares Probelms
28.01. TORO (Part 1) 19.pdf
19-4up.pdf
MP4 472MB
MP4 270MB
YouTube
Fill out evaluation online! Non-linear Constraint Network Optimization for Efficient Map Learning
04.02. TORO (Part 2) see above MP4 584MB
MP4 339MB
YouTube
--- Non-linear Constraint Network Optimization for Efficient Map Learning
04.02. Front-Ends for Graph-Based SLAM (Part 1) 20.pdf
20-4up.pdf
MP4 392MB
MP4 228MB
YouTube
Sheet10
Octave-Code
---
11.02. Front-Ends for Graph-Based SLAM (Part 2) see above MP4 582MB
MP4 338MB
YouTube
--- Recognizing Places using Spectrally Clustered Local Matches
11.02. Summary 21.pdf
21-4up.pdf
MP4 575MB
MP4 335MB
YouTube
--- ---

In case you need to revisit material about Gaussians or a reference for matrix operations:

  • Marginalization and Conditioning of Gaussians (taken from Eustice et al, IROS 05), png
  • K. Murphy: Gaussian, pdf
  • Petersen and Pedersen: The Matrix Cookbook, pdf

Relevant Literature for the Course

Most of the literature is available as PDF files for free, but not the "Probabilistic Robotics" book. You find it in the TF library.
  • Thrun, Burgard, Fox: Probabilistic Robotics, MIT Press, 2005, website
  • Springer Handbook on Robotics, Chapter on Simultaneous Localization and Mapping (Chapt. 37 in 1st edition)
  • Schoen and Lindsten: Manipulating the Multivariate Gaussian Density, 2011, pdf
  • Welch and Bishop: Kalman Filter Tutorial, 2011, pdf
  • Julier and Uhlmann: A New Extension of the Kalman Filter to Nonlinear Systems, 1995, pdf
  • Thrun, Liu, Koller, Ng, Ghahramani, Durrant-Whyte: Simultaneous Localization and Mapping With Sparse Extended Information Filters, 2004. pdf
  • Eustice, Walter, Leonard: Sparse Extended Information Filters: Insights into Sparsification, IROS, 2005. pdf
  • Montemerlo, Thrun, Kollar, Wegbreit: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem, 2002, pdf
  • Montemerlo, Thrun: Simultaneous Localization and Mapping with Unknown Data Association Using FastSLAM, 2003, pdf
  • Grisetti, Stachniss, Burgard: Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters, 2007, pdf
  • Stachniss, Grisetti, Burgard, Roy: Analyzing Gaussian Proposal Distributions for Mapping with Rao-Blackwellized Particle Filters, 2007, pdf
  • Madsen, Nielsen, Tingleff: Methods for Non-Linear Least Squares Probelms, 2004, pdf
  • Grisetti, Kuemmerle, Stachniss, Burgard: A Tutorial on Graph-based SLAM, 2010, pdf
  • Grisetti, Kuemmerle, Stachniss, Frese, Hertzberg: Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping, 2010, pdf
  • Olson, Agarwal: Inference on Networks of Mixtures for Robust Robot Mapping, 2013, pdf
  • Olson, Leonard, Teller: Fast Iterative Optimization of Pose Graphs with Poor Initial Estimates, 2006, pdf
  • Grisetti, Stachniss, Burgard: Non-linear Constraint Network Optimization for Efficient Map Learning, 2009, pdf
  • Olson: Recognizing Places using Spectrally Clustered Local Matches, 2009, pdf
Benutzerspezifische Werkzeuge