Robohub.org
 

Building topological maps to get around


by
31 August 2011



share this:

Service robots entering our homes will need to map their environment and figure out their location as they move around. Previous articles discussed Self-Localization And Mapping (SLAM) approaches that give accurate measurements regarding the location of the robot and objects in the environment. Such so called “metric” approaches can be useful for robot tasks that require high accuracy, such as placing a cup in an exact location.

Instead, the “topological” approach represents the environment as places (nodes) and paths between places as edges. Robots can localize by finding the node where they are currently positioned. The advantage of this approach is that large amounts of data can be stored as nodes and edges and noisy sensors can be used to grossly map the environment. Furthermore, for human robot interactions it is sometimes more useful for the robot to know in what room it is (e.g. kitchen node) rather than a cartesian coordinate.

Following this idea, Choi et al. present a method for autonomous topological modeling and localization in home environments using only low-cost sonar sensors. Experiments were conducted using a Pioneer 3-DX differential drive robot (see picture below) equipped with 12 Murata MA40B8 sonar sensors in a 11.4 m × 8.7 m home environment of several rooms containing items of furniture.

As a first step, the robot was manually guided along an arbitrary path at an average speed of about 0.15 m/s while acquiring sensor data at a rate of 4 Hz. Based on the sonar data, the robot marks a grid map with regions that have obstacles and those that don’t. The grid map is then partitioned into several convex subregions that represent the nodes in the environment. The result is a topological map as can be seen below. As a second experiment, the robot is again guided through the environment and asked to identify its node location, even in situations where furniture has been moved around. Results show that the proposed method provides reliable modeling and localization using sparse and noisy sonar data.

Experimental results of the autonomous topological modeling process: autonomous subregion extractions (each subregion is a different color) and the corresponding topological models.

Although the proposed method was developed for sonar sensors, it can also be applied to any type of sensor that generates grid maps (e.g., laser range finders or stereo vision sensors).




Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory
Sabine Hauert is President of Robohub and Associate Professor at the Bristol Robotics Laboratory





Related posts :



Robot see, robot do: System learns after watching how-tos

  14 May 2025
Researchers have developed a new robotic framework that allows robots to learn tasks by watching a how-to video

AI-powered robots help tackle Europe’s growing e-waste problem

  12 May 2025
EU-funded researchers have developed adaptable robots that could transform the way we recycle electronic waste, benefiting both the environment and the economy.

Robot Talk Episode 120 – Evolving robots to explore other planets, with Emma Hart

  09 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Emma Hart from Edinburgh Napier University about algorithms that 'evolve' better robot designs and control systems.

Robot Talk Episode 119 – Robotics for small manufacturers, with Will Kinghorn

  02 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Will Kinghorn from Made Smarter about how to increase adoption of new tech by small manufacturers.

Multi-agent path finding in continuous environments

  01 May 2025
How can a group of agents minimise their journey length whilst avoiding collisions?

Interview with Yuki Mitsufuji: Improving AI image generation

  29 Apr 2025
Find out about two pieces of research tackling different aspects of image generation.

Robot Talk Episode 118 – Soft robotics and electronic skin, with Miranda Lowther

  25 Apr 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Miranda Lowther from the University of Bristol about soft, sensitive electronic skin for prosthetic limbs.

Interview with Amina Mević: Machine learning applied to semiconductor manufacturing

  17 Apr 2025
Find out how Amina is using machine learning to develop an explainable multi-output virtual metrology system.



 

Robohub is supported by:




Would you like to learn how to tell impactful stories about your robot or AI system?


scicomm
training the next generation of science communicators in robotics & AI


©2025.05 - Association for the Understanding of Artificial Intelligence


 












©2025.05 - Association for the Understanding of Artificial Intelligence