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, and 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

Organizer: Wolfram Burgard

Tutors: Daniel Büscher, Lukas Luft

Teaching is done in English.

This is an online lecture. That means students are responsible for studying the contents of the lecture and working on the assignments in a self-organized manner. Every week, the assignments are discussed during the exercise session.

Course introduction Date: Thursday, October 25, 16:30 to 18:00 Location: building 80, room 00-021

Exercise sessions Date: on Thursdays, 16:00 to 17:30, starting on November 8 Location: building 101, Seminar 00-010/014

Exam Type: oral Dates: 27.2., 12.3., 13.3., 14.3. and 22.3.2019 between 15:30 and 17:30

There is a QnA forum for questions available.

Recordings, slides, homework assignments, and additional material is available on this website.

The lecture is part of the area Cognitive Technical Systems in the CS master program.

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

Schedule

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

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

Gian Diego Tipaldi: Notes on Univariate Gaussian Distributions and One-Dimensional Kalman Filters, 2015, 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 Problems, 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

Most of the literature is available as PDF files for free, but not the "Probabilistic Robotics" book. You find it in the TF library.