An optimal pose estimator for map-based mobile robot dynamic localization: experimental comparison with the EKF

Borges, Geovany A.; Aldon, Marie-José; Gil, Thierry

doi:10.1109/robot.2001.932837

Cited by 10 publications

(13 citation statements)

References 11 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been widely used for self-localization and SLAM-problems [6,14], whenever pure KFs cannot be applied. The assumption that observations are linear functions of the state and that the current state is a linear function of the previous state, is crucial for the correctness of KFs.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

“…8. 6. In a first step the egocentric object position e is transformed into e θ using the rotational distribution function of the robot s(θ):…”

Section: Transformation From Egocentric Into Allocentric Coordinatesmentioning

confidence: 99%

“…6. In the last experiments we compared the modeling error for higher self-localization and local player model errors.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

Göhring

2010

Künstl Intell

View full text Add to dashboard Cite

Mobile autonomous robotics is a very young and complex field of research. Only in recent decades have robots become able to explore, to move, navigate and to interact with their environment.Since the world is uncertain and since robots can only gain partial information about it, probabilistic navigation algorithms have become very popular whenever a robot has to localize itself or surrounding objects. Furthermore, cooperative exploration and localization approaches have become very relevant lately, as robots begin to act not just alone but in groups. Within this thesis a new approach using the concept of spatial percept-relations for cooperative environment modeling is presented and evaluated. As a second contribution, constraint based localization techniques will be introduced for having a robot or a group of robots efficiently localized and to model their environment.v Zusammenfassung Die mobile Robotik stellt ein sehr junges und komplexes Forschungsfelder unserer Zeit dar. Innerhalb der letzten Jahrzehnte wurde es Robotern möglich, sich innerhalb ihrer Umgebung zu bewegen, zu navigieren und mit ihrer Umwelt zu interagieren.Aufgrund der Tatsache, dass die Welt von Unsicherheit geprägt ist und ein Roboter immer nur partielle Information über sie erhalten kann, wurden probabilistische Navigationsverfahren entwickelt, mit denen sich Roboter lokalisieren und Objekte ihrer Umgebung modellieren können. Weiterhin wurden in letzter Zeit Verfahren untersucht, die die kooperative Exploration der Umgebung durch eine Gruppe von Robotern zum Ziel haben. In der vorliegenden Arbeit wird ein neuartiges Konzept, welches sich Perzeptrelationen für die kooperative Umweltmodellierung zu Nutze macht, vorgestellt und evaluiert. Einen zweiten Beitrag der Arbeit stellen constraintbasierte Lokalisierungstechniken dar, die es einem oder mehreren Robotern auf effiziente Art und Weise ermöglichen, sich zu lokalisieren und ihre Umwelt zu modellieren.vii

show abstract

Section: Extended Kalman Filtermentioning

confidence: 99%

“…8. 6. In a first step the egocentric object position e is transformed into e θ using the rotational distribution function of the robot s(θ):…”

Section: Transformation From Egocentric Into Allocentric Coordinatesmentioning

confidence: 99%

See 1 more Smart Citation

Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

Göhring

2010

Künstl Intell

View full text Add to dashboard Cite

show abstract

“…Some implementations do not use an explicit local map representation, as in [3] which processes independently geometrical features extracted from data obtained with a multisensory system. In order to minimize the robot motion effects on exteroceptive sensor data, motion correction [3,17], sensor bias correction [12,17], data synchronization by software [12], and computing time compensation [12,14] procedures should be used.…”

Section: Principles Of Pose Estimation Using 2d Geometrical Mapsmentioning

confidence: 99%

“…We have developed an approach for mobile robot localization based on non-linear optimization in [14,16,17]. In these papers, guaranteed optimal estimation of robot pose is computed from geometrical map matches using a batch, non-iterated, weighted least-squares algorithm.…”

Section: Introductionmentioning

confidence: 99%