SWARM
Multi-robot cooperative map building in unknown environment considering estimation uncertainty
Tao Tong, Yalou Huang, Yuan Jing
- Year
- 2008
- Citations
- 20
Abstract
This paper focuses on the multi-robot cooperative simultaneous localization and map building (SLAM) problem and proposes an approach to compute the destination points for the robots which explore in the environment. This approach considers the efficiency and the accuracy of global map building. The approach makes the robots finish the exploration and build the map with high quality. Extended Kalman Filter (EKF) algorithm is applied to estimate the locations of the robots and the positions of the landmarks. The simulation results show the effectiveness of the proposed approach.
Keywords
Extended Kalman filterRobotSimultaneous localization and mappingComputer scienceKalman filterArtificial intelligenceMobile robotComputer visionGlobal MapQuality (philosophy)
Related papers
OTHER
📊 26,957 cites
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
PERCEPTION
📊 22,245 cites
Artificial intelligence: a modern approach
1995
OTHER
📊 18,993 cites
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
SWARM
📊 14,853 cites
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002