Robot Mapping - WS 2014/15

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, mapping and localization. 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, and data association. 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

Lecturers: Gian Diego Tipaldi, Luciano Spinello, and Wolfram Burgard
Tutors: Mladen Mazuran and Andreas Eitel
Rooms and times:
- Mon, 16-18 in building 51, HS 03-026
- Thu, 16-18 in building 51, SR 03-026
There is a QnA forum for questions available.
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.

Update

Join us on the google group

Misc

Teaching material and exercises are based on the previous course taught by Cyrill Stachniss.

Schedule

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Date	Topic	Slides	Recordings	Homework Assignment	Relevant Literature
20.10.	Course Introduction	00.pdf	---	---	---
20.10.	Introduction to Robot Mapping	01.pdf	---	---	Springer Handbook on Robotics Chapter 37.1 + 37.2
23.10.	Homogenous Coordinates	02.pdf	---	Sheet1 Octave-Code Octave Help	Wiki page
28.10.	Bayes Filter and Related Models (Summary of Introduction to Mobile Robotics)	03.pdf	MP4	Sheet2	Introduction to Mobile Robotics lecture Probabilistic Robotics Book, Chapters 2, 5, 6
28.10.	Kalman Filter and Extended Kalman Filter (EKF)	04.pdf	MP4	Sheet3	Probabilistic Robotics Book, Chapter 3.1-3.3 Manipulating the Multivariate Gaussian Density Kalman Filter Tutorial by Welch and Bishop
03.11.	EKF SLAM	05.pdf	MP4	Sheet4 Octave-Code	Probabilistic Robotics Book, Chapter 10
10.11.	Unscented Kalman Filter (UKF)	06.pdf	MP4	Sheet5 Octave-Code	Probabilistic Robotics Book, Chapter 3.4
10.11.	Extended Information Filter (EIF)	07.pdf	MP4	---	Probabilistic Robotics Book, Chapter 3.5
17.11.	Sparse EIF SLAM - Part 1	08.pdf	MP4	---	Probabilistic Robotics Book, Chapter 12.1-12.7 SEIF Paper
24.11.	Sparse EIF SLAM - Part 2	08.pdf	MP4	---	Probabilistic Robotics Book, Chapter 12.1-12.7 SEIF Paper SEIF: Insights on Sparsification
24.11.	Summary on the Kalman Filter and its Friends for SLAM	09.pdf	MP4	Sheet6 Octave-Code	---
01.12.	Grid Maps	10.pdf	MP4	---	Probabilistic Robotics Book, Chapter 4.2, 9.1-9.2 A method for Registration of 3-D Shapes Real-Time Correlative Scan Matching
01.12.	Short Introduction to the Particle Filter and PF Localization	11.pdf	MP4	Sheet7 Octave-Code-1 Octave-Code-2	Probabilistic Robotics Book, Chapter 8.3
08.12.	FastSLAM	12.pdf	MP4	Sheet8 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
15.12.	Grid-Based SLAM with Rao-Blackwellized Particle Filters	13.pdf	MP4	---	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
12.01.	Least-Squares	14.pdf	MP4	Sheet9 Octave-Code	Methods for Non-Linear Least Squares Probelms Wiki:Gauss-Newton Probabilistic Robotics Book, Chapter 11.4
19.01.	Least-Squares Approach to SLAM	15.pdf	MP4	Sheet10 Octave-Code	A Tutorial on Graph-Based SLAM Probabilistic Robotics Book, Chapter 11 Methods for Non-Linear Least Squares Probelms
26.01.	Hierarchical Pose-Graphs for Online Mapping	16.pdf	MP4	---	A Tutorial on Graph-Based SLAM Blackboard notes Probabilistic Robotics Book, Chapter 11 Hierarchical Optimization on Manifolds for Online 2D and 3D Mapping
26.01.	Graph-Based SLAM with Landmarks	17.pdf	MP4	---	Probabilistic Robotics Book, Chapter 11 Methods for Non-Linear Least Squares Probelms
02.02.	Max-Mixture and Robust Least Squares for SLAM	18.pdf	MP4	---	Inference on Networks of Mixtures for Robust Robot Mapping Methods for Non-Linear Least Squares Problems
02.02.	Stochastic Gradient Descent for SLAM	19.pdf	MP4	---	Non-linear Constraint Network Optimization for Efficient Map Learning Fast Iterative Optimization of Pose Graphs with Poor Initial Estimates
09.02.	Front-Ends for Graph-Based SLAM	20.pdf	MP4	---	FLIRT -- Interest Regions for 2D Range Data Recognizing Places using Spectrally Clustered Local Matches
09.02.	Short Summary	21.pdf	MP4	---	---

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

Basic probability rules, pdf
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
Agarwal, Tipaldi, Spinello, Stachniss, Burgard: Robust Map Optimization Using Dynamic Covariance Scaling, 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
Tipaldi, Arras: FLIRT -- Interest Regions for 2D Range Data, 2010, pdf

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