CSCE 774 Robotics Systems
Spring 2017
From the surface of Mars and the far reaches of our Solar System to the bottom of the ocean, robots are helping us expand our scientific understanding of the universe. In our everyday life, devices that gather information and interact with the environment are ever-present, taking over many of the undesirable or dangerous tasks that humans had to perform previously. Fundamental to all these robotic devises is their ability to sense the environment, reason about it, and then plan and safely execute the best action. There are three fundamental challenges in robotics:
  • Localization
  • Mapping
  • Path Planning
This course is designed as an advance graduate course with a focus on the challenges of Localization and Mapping. Different sensors will be investigated with an emphasis on Visual and Inertial data. The first few lectures will provide necessary background on Localization, Mapping, SLAM, and state estimation; then we will switch to a reading group mode. Each lecture will focus on a paper, a student will present the main ideas, and then we will discuss the paper. Every student is expected to have read the paper and bring their understanding in the discussion.

Instructor

Evaluation:

  • Assigniments (3) 10% each: 30%
  • Final Project 20%
  • Class participation 20%
  • Presentations 30%
 

Student Presentations


Resources

Paper
 Resources Presenter Presentation Date Remarks
R. Smith, M. Self, and P. Cheeseman. "Estimating uncertain spatial relationships in robotics", Autonomous robot vehicles, 1990 Nare 02/14/2017 One of the first formulations of SLAM
H. Durrant-Whyte and T. Bailey, "Simultaneous Localisation and Mapping: Part I", RAM, 2006 Jordan 02/16/2017 Overview paper first part
T. Bailey and H. Durrant-Whyte, "Simultaneous Localisation and Mapping: Part II", RAM, 2006 Preston 02/21/2017 Overview paper second part
Simon J. Julier Jeffrey K. Uhlmann. A New Extension of the Kalman Filter to Nonlinear Systems Shannon 02/23/2017
F. Lu and E. Milios, "Globally consistent range scan alignment for environment mapping". Autonomous Robots October 1997, Volume 4, Issue 4, pp 333-349 Jason 02/28/2017
S. Thrun and M. Montemerlo, "The GraphSLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures", International Journal on Robotics Research, v. 25, no 5/6, pp. 403-430, 2005 A tutorial Anton 03/02/2017
G. Grisetti, C. Stachniss, and W. Burgard. Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters OpenSLAM code repository Zheqing 03/14/2017
R. Kummerle et al., "G2O: A general framework for graph optimisation", International Conference of Robotics andd Automation 2011 OpenSLAM code repository Nick 03/16/2017
Frank Dellaert and Michael Kaess. 2006. Square Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing. Int. J. Rob. Res. 25, 12 (December 2006), 1181-1203 Hazhar 03/21/2017
D. Scaramuzza, F. Fraundorfer. Visual Odometry: Part I - The First 30 Years and Fundamentals IEEE Robotics and Automation Magazine, Volume 18, issue 4, 2011. Presentation Sharmin Rahman 01/12/2017 Overview paper first part
F. Fraundorfer, D. Scaramuzza. Visual odometry: Part II - Matching, robustness, optimization, and applications. IEEE Robotics and Automation Magazine, Volume 19, issue 2, 2012. Presentation Nare 03/23/2017 Overview paper second part
Raul Mur-Artal, J. M. M. Montiel and Juan D. Tardos. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, 2015 Code Shannon 03/23/2017
Johannes L. Schonberger, Jan-Michael Frahm; Structure-From-Motion Revisited The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4104-4113 Code Nick 03/28/2017
Bill Triggs, Philip F. McLauchlan, Richard I. Hartley, and Andrew W. Fitzgibbon. "Bundle Adjustment - A Modern Synthesis". Lecture Notes in Computer Science, Vision Algorithms: Theory and Practice Volume 1883, 2000, pp 298-372
A.I. Mourikis and S.I. Roumeliotis, "A Multi-State Constrained Kalman filter for Vision-aided Inertial Navigation", In Proc. 2007 IEEE International Conference on Robotics and Automation, Rome, Italy, Apr. 10-14, pp. 3565-3572 Zheqing 03/30/2017
Forster, Christian, Pizzoli, Matia, and Scaramuzza, Davide. SVO: Fast Semi-Direct Monocular Visual Odometry. IEEE International Conference on Robotics and Automation, 2014 Code repository Jason 04/04/2017
J. Engel, T. Schops, D. Cremers. LSD-SLAM: Large-Scale Direct Monocular SLAM. European Conference on Computer Vision (ECCV), 2014. Code repository Preston 04/04/2017
E. Jones and S. Soatto. Visual-Inertial Navigation, Mapping and Localization: A Scalable Real-Time Causal Approach. International Journal of Robotics Research, January 2011. Darren 04/06/2017
Stefan Leutenegger, Simon Lynen, Michael Bosse, Roland Siegwart and Paul Timothy Furgale. Keyframe-based visual–inertial odometry using nonlinear optimization. The International Journal of Robotics Research` Code Hazhar 04/11/2017
A tutorial on the basic math ideas encountered Anton 04/13/2017
M. Cummins and P. Newman, "FAB-MAP: Probabilistic localization and mapping in the space of appearance", International Journal of Robotics Research, 2008
M. Cummins and P. Newman, "FAB-MAP: Probabilistic localization and mapping in the space of appearance", International Journal of Robotics Research, 2008 Official Page Jordan 04/18/2017 Appearance based mapping
Richard A. Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J. Davison, Pushmeet Kohli, Jamie Shotton, Steve Hodges, and Andrew Fitzgibbon. "KinectFusion: Real-Time Dense Surface Mapping and Tracking". (ISMAR 2011, Best paper award!) Darren 04/18/2017
Other Papers
H. W. Sorenson. Least-squares estimation from Gauss to Kalman. IEEE Spectrum, 1970 Introduction to state estimation
F. Lu and E. Milios, "Robot pose estimation in unknown environments by matching 2d range scans". IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.935--938, 21-23 Jun 1994
Michael Montemerlo, Sebastian Thrun, Daphne Koller, and Ben Wegbreit. FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI) 2003

M. Kaess, A. Ranganathan, and F. Dellaert, "iSAM: Incremental Smoothing and Mapping" IEEE Transactions on Robotics, vol. 24, no. 6, Dec. 2008, pp. 1365-1378,
M. Kaess, H. Johannsson, R. Roberts, V. Ila, J.J. Leonard, and F. Dellaert. "iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree". International Journal of Robotics Research, vol. 31, Feb. 2012, pp. 217-236. Details
D. Nister et. al., "Visual odometry for ground vehicle applications", JFR 2006
G. Klein and D. Murray, "Parallel Tracking and Mapping for Small AR Workspaces", ISMAR 2007 PTAM
Michael Milford, Gordon Wyeth: Persistent navigation and mapping using a biologically inspired SLAM system, International Journal of Robotics Research, 2010 OpenSLAM code repository
G. Sibley et. al., "Adaptive relative bundle adjustment", Robotics: Science and Systems, 2009
Florian Shkurti, Ioannis Rekleitis, Milena Scaccia, Gregory Dudek. State estimation of an underwater robot using visual and inertial information. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011
Mahon, I., Pizarro, O., Johnson-Roberson, M., Friedman, A., Williams, S., Henderson, J. Reconstructing Pavlopetri: mapping the world's oldest submerged town using stereo-vision. IEEE International Conference on Robotics and Automation, 2011 TBD

Lecture notes are posted here

A Gentle Introduction to ROS

Exploration Video

Particle Filter video

Particle Filter Tutorial

Assignments

Description
 code
 worlds/data files
 Due date
pf2dlocalizer.zip assignment_1b_no_map.bag
  • Part I: 02 Feb. 2017
  • Part II: 16 Feb. 2017
Assignment 2: Bibliography Search (team)

16 Mar. 2017


10 Apr. 2017