Robohub.org
 

Cooperative localization using Kalman filtering

by
26 November 2010



share this:

Kalman filters are used in robotics to correct measurement errors. Imagine trying to precisely predict the position of an outdoor robot. The robot is equipped with a GPS and is able to measure the speed of its wheels (odometry). Only using GPS leads to measurements that are not precise while using only odometry leads to increasingly wrong estimates. Instead, what a Kalman filter does is fuse the information from odometry with the GPS measurements. This is done by, at each step of the robot control, predicting future sensor readings based on the commands given to the robot. The difference between the predicted sensor readings and the actual sensor readings is then used to update the filter. In this manner, the robot is able to improve its position estimate over time.

In work by Huang et al., groups of indoor robots attempt to estimate their global position and orientation using a special type of Kalman filter called the “Extended Kalman Filter”. Since they do not have access to GPS or landmarks in the environment, robots “cooperatively localize” by using odometry and measuring their relative position to neighboring robots. However, Kalman filters can be challenged when the measurements they make do not give them enough information with respect to what they are trying to predict. For example, sensor measurements might only provide meaningful information to correct position estimates but not global orientation. In these cases, the system is “not observable” and the Kalman filter can result in inconsistencies.

To overcome this challenge Huang et al. propose two ways of extending Kalman filters so as to constrain the observability of the system. Results are given in simulation and using four Pioneer I robots that were able to successfully estimate their pose. Odometry measurements were derived from wheel encoders and relative position was computed using an overhead camera thanks to the rectangular tags on each robot shown in the figure below. Results show that both developed algorithms outperform standard extended Kalman filters.

In the future, researchers hope to extend their approach to 3D localization.




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 :



Flocks of assembler robots show potential for making larger structures

Researchers make progress toward groups of robots that could build almost anything, including buildings, vehicles, and even bigger robots.
25 November 2022, by

Holiday robot wishlist for/from Women in Robotics

Are you looking for a gift for the women in robotics in your life? Or the up and coming women in robotics in your family? Perhaps these suggestions from our not-for-profit Women in Robotics organization will inspire!
24 November 2022, by and

TRINITY, the European network for Agile Manufacturing

The Trinity project is the magnet that connects every segment of agile with everyone involved, creating a network that supports people, organisations, production and processes.
20 November 2022, by

Fighting tumours with magnetic bacteria

Researchers at ETH Zurich are planning to use magnetic bacteria to fight cancerous tumours. They have now found a way for these microorganisms to effectively cross blood vessel walls and subsequently colonise a tumour.
19 November 2022, by

Combating climate change with a soft robotics fish

We have fabricated a 3D printed, cable-actuated wave spring tail made from soft materials that can drive a small robot fish.
17 November 2022, by

#IROS2022 best paper awards

Here we bring you the papers that received an award this year at IROS in case you missed them.
14 November 2022, by





©2021 - ROBOTS Association


 












©2021 - ROBOTS Association