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 :



Robot Talk Episode 64 – Rav Chunilal

In the latest episode of the Robot Talk podcast, Claire chatted to Rav Chunilal from Sellafield all about robotics and AI for nuclear decommissioning.
31 December 2023, by

AI holidays 2023

Thanks to those that sent and suggested AI and robotics-themed holiday videos, images, and stories. Here’s a sample to get you into the spirit this season....
31 December 2023, by and

Faced with dwindling bee colonies, scientists are arming queens with robots and smart hives

By Farshad Arvin, Martin Stefanec, and Tomas Krajnik Be it the news or the dwindling number of creatures hitting your windscreens, it will not have evaded you that the insect world in bad shape. ...
31 December 2023, by

Robot Talk Episode 63 – Ayse Kucukyilmaz

In the latest episode of the Robot Talk podcast, Claire chatted to Ayse Kucukyilmaz from the University of Nottingham about collaboration, conflict and failure in human-robot interactions.
31 December 2023, by

Interview with Dautzenberg Roman: #IROS2023 Best Paper Award on Mobile Manipulation sponsored by OMRON Sinic X Corp.

The award-winning author describe their work on an aerial robot which can exert large forces onto walls.
19 November 2023, by

Robot Talk Episode 62 – Jorvon Moss

In the latest episode of the Robot Talk podcast, Claire chatted to Jorvon (Odd-Jayy) Moss from Digikey about making robots at home, and robot design and aesthetics.
17 November 2023, by





©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association