CSCE 774 Robotics Systems
Fall 2023
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

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 Anzhelika Kolinko 09/06/2023 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 Overview paper second part
R. Smith, M. Self, and P. Cheeseman. "Estimating uncertain spatial relationships in robotics", Autonomous robot vehicles, 1990 Presentation Lily 10/04/2023 One of the first formulations of SLAM
Visual Inertial Tutorial by Patrick Geneva
Visual Inertial Tutorial by Stergios Roumeliotis
The GraphSLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures
 Presentation Keith Lewandowski 10/25/2023
A Tutorial on Graph-Based SLAM
 Presentation Malcolm Peterson 10/30/2023
Indirect Kalman Filter for 3D Attitude Estimation by Nikolas Trawny and Stergios I. Roumeliotisr
 Presentation Titon Barua 11/06/2023 and 11/08/2023 Remarks
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance by Mark Cummins and Paul Newman
 Presentation David (Zifei) Zhong 11/13/2023 Remarks
Keyframe-Based Visual-Inertial SLAM Using Nonlinear Optimization by Stefan Leutenegger, Paul Furgale, Vincent Rabaud, Margarita Chli, Kurt Konolige, and Roland Siegwart
 Presentation Ibrahim Salman 11/15/2023 Remarks
A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation
 Presentation Matthew Duffell-Hoffman 11/20/2023 Remarks
iSAM: Incremental Smoothing and Mapping
iSAM2: Incremental Smoothing and Mapping Using the Bayes Tree
 Presentation Resources Alexander Vereen 11/27/2023 Remarks

Lecture notes are posted here

A Gentle Introduction to ROS

Exploration Video

Particle Filter video

Particle Filter Tutorial

CALIBRATION DESK REFERENCE

Assignments

Description
 code
 worlds/data files
 Due date

10/30/2023
Assignment 2: Camera Calibration
GX010181.bag
GX010182.bag
GX010183.bag
aprilgrid_5x7Canvas.yml
 11/15/2023