Robohub.org
 

RFID-based global positioning


by
14 August 2010



share this:

Having a robot figure out its global position is required in many real world applications, it’s also one of the biggest challenges in robotics.

The easiest approach is to have a robot blindly keep track of its movements (odometry) from a known starting position. Odometry alone however quickly results in an add-up of errors that make the localization unusable.

To help the robot along the way, Boccadoro et al. propose to place passive Radio-Frequency IDentification (RFID) tags in known positions in the environment. These smart tags are interesting because they are typically low cost and require no energy to function. Robots equipped with RFID readers can detect a tag within a 1m range, although with a lot of noise. Algorithms are then needed to combine the robot’s sensors, in this case odometry, with the noisy RFID readings to precisely estimate its global position.

For this purpose, two types of Kalman Filters are implemented and compared to a Particle Filter method that typically has much larger computational cost. Experiments were conducted using a Pioneer P3-DX driving around a corridor equipped with 6 RFID tags.

Results show that the first method is fast but imprecise when tags are sparse (figure left). The second approach has higher computation requirements than the first but is able to obtain estimates as good as the Particle Filter method with few tags (figure right).

The path reconstructed through the various methods proposed: a red line is used to represent the estimation of the second loop of the robot path, the green line is used for the last loop; the line in blue is ground truth.

In the future, authors hope to investigate the optimal placement of RFID tags to achieve even better position estimates.




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 Talk Episode 126 – Why are we building humanoid robots?

  20 Jun 2025
In this special live recording at Imperial College London, Claire chatted to Ben Russell, Maryam Banitalebi Dehkordi, and Petar Kormushev about humanoid robotics.

Gearing up for RoboCupJunior: Interview with Ana Patrícia Magalhães

and   18 Jun 2025
We hear from the organiser of RoboCupJunior 2025 and find out how the preparations are going for the event.

Robot Talk Episode 125 – Chatting with robots, with Gabriel Skantze

  13 Jun 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Gabriel Skantze from KTH Royal Institute of Technology about having natural face-to-face conversations with robots.

Preparing for kick-off at RoboCup2025: an interview with General Chair Marco Simões

and   12 Jun 2025
We caught up with Marco to find out what exciting events are in store at this year's RoboCup.

Interview with Amar Halilovic: Explainable AI for robotics

  10 Jun 2025
Find out about Amar's research investigating the generation of explanations for robot actions.

Robot Talk Episode 124 – Robots in the performing arts, with Amy LaViers

  06 Jun 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Amy LaViers from the Robotics, Automation, and Dance Lab about the creative relationship between humans and machines.

Robot Talk Episode 123 – Standardising robot programming, with Nick Thompson

  30 May 2025
In the latest episode of the Robot Talk podcast, Claire chatted to Nick Thompson from BOW about software that makes robots easier to program.

Congratulations to the #AAMAS2025 best paper, best demo, and distinguished dissertation award winners

  29 May 2025
Find out who won the awards presented at the International Conference on Autonomous Agents and Multiagent Systems last week.



 

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