Tag Archives: LCAS

Robotics Research Seminar 24/5/17: “Making Robust SLAM Solvers for Autonomous Mobile Robots”

We invite everybody to attend the robotics research seminar, organised by L-CAS, on Wednesday 24/5/2017:

grisettiDr Giorgio Grisetti, DIAG, University of Rome “Sapienza”:

Making Robust SLAM Solvers for Autonomous Mobile Robots

  • WHERE: AAD1W11, Lecture Theatre (Art, Architecture and Design Building), Brayford Pool Campus
  • WHEN: Wednesday 24th May 2017, 3:00 – 4:00 pm

ABSTRACT:

In robotics, simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it.

SLAM is an essential enabling technology for building truly autonomous robots that can operate in an unknown environment. The last three decades have seen substantial research in the field and modern SLAM systems are able to cope easily with operating conditions that in the past were regarded as challenging if not impossible to deal with.

This consideration might support the statement that SLAM is a closed problem. However a closer look at the contributions presented in the most relevant conferences and journals in robotics reveals that the papers on SLAM are still numerous and the community is large. Would this be the case if an off-the shelf solution that works all the time were available?

Non-experts that approach the problem, or even want to get one of the state-of-the-art systems running, often encounter problems and get performances that are far from the ones reported in the papers.  This is usually because the person using the system is not the person designing the system.  An open box approach that aims at solving the problems by modifying an existing pipeline is often hard to implement due to the complexity of modern SLAM systems.

In this talk we will overview the history of SLAM and we will outline some of the challenges in designing robust SLAM systems, and most importantly forming robust SLAM solvers.

Furthermore, we will also present PRO-SLAM (SLAM from a programmer’s perspective), a simplistic open-source pipeline that competes with state-of-the art Stereo Visual SLAM systems while focusing on simplicity to support teaching.

https://gitlab.com/srrg-software/srrg_proslam

BAXTER Taught-In Activity

Take a look at how well our robot BAXTER is coming along.

This is the very first demo of our new Baxter robot using taught-in trajectories to manipulate some objects. Not much state-of-the-art science in this one, but a feasibility study of what’s possible with off-the-shelf components.

 

Let us know what you think in the comments below