Robohub.org
 

Using geometry to help robots map their environment

by
26 February 2014



share this:

This post is part of our ongoing efforts to make the latest papers in robotics accessible to a general audience.

To get around unknown environments, most robots will need to build maps. To help them do so, robots can use the fact that human environments are often made of geometric shapes like circles, rectangles and lines. The latest paper in Autonomous Robots presents a flexible framework for geometrical robotic mapping in structured environments.

Most human designed environments, such as buildings, present regular geometrical properties that can be preserved in the maps that robots build and use. If some information about the general layout of the environment is available, it can be used to build more meaningful models and significantly improve the accuracy of the resulting maps. Human cognition exploits domain knowledge to a large extent, usually employing prior assumptions for the interpretation of situations and environments. When we see a wall, for example, we assume that it’s straight. We’ll probably also assume that it’s connected to another orthogonal wall.

This research presents a novel framework for the inference and incorporation of knowledge about the structure of the environment into the robotic mapping process. A hierarchical representation of geometrical elements (features) and relations between them (constraints) provides enhanced flexibility, also making it possible to correct wrong hypotheses. Various features and constraints are available, and it is very easy to add even more.

A variety of experiments with both synthetic and real data were conducted. The map below was generated from data measured by a robot navigating Killian Court at MIT using a laser scanner, and allows the geometrical properties of the environment to be well respected. You can easily tell that features are parallel, orthogonal and straight where needed.

map2

For more information, you can read the paper Feature based graph-SLAM in structured environments ( P. de la Puente and D. Rodriguez-Losada , Autonomous Robots – Springer US, Feb 2014) or ask questions below! 



tags: , ,


Autonomous Robots Blog Latest publications in the journal Autonomous Robots (Springer).
Autonomous Robots Blog Latest publications in the journal Autonomous Robots (Springer).





Related posts :



World Robotics 2023 report: Asia ahead of Europe and the Americas

The new World Robotics report recorded 553,052 industrial robot installations in factories around the world – a growth rate of 5% in 2022, year-on-year. By region, 73% of all newly deployed robots were installed in Asia, 15% in Europe and 10% in the Americas.

#IROS2023: A glimpse into the next generation of robotics

The 2023 EEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023) kicks off today at the Huntington Place in Detroit, Michigan.
01 October 2023, by

Robot Talk Episode 55 – Sara Adela Abad Guaman

In the first episode of the new season, Claire chatted to Dr. Sara Adela Abad Guaman from University College London about adaptable robots inspired by nature.
30 September 2023, by

A short guide to Multidisciplinary Research

How and Why would I consider colliding two opposite disciplines in my research.
27 September 2023, by

Robo-Insight #5

In this fifth edition, we are excited to feature robot progress in human-robot interaction, agile movement, enhanced training methods, soft robotics, brain surgery, medical navigation, and ecological research. 
25 September 2023, by

Soft robotic tool provides new ‘eyes’ in endovascular surgery

The magnetic device can help visualise and navigate complex and narrow spaces.





©2021 - ROBOTS Association


 












©2021 - ROBOTS Association