Recursive unscented Kalman filtering based SLAM using a large number of noisy observations
- Authors
- Lee, Seongsoo; Lee, Sukhan; Kim, Dongsung
- Issue Date
- Dec-2006
- Publisher
- INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
- Keywords
- real-time SLAM; recursive unscented Kalman filtering; stochastic SLAM; vision-based SLAM
- Citation
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.4, no.6, pp.736 - 747
- Journal Title
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
- Volume
- 4
- Number
- 6
- Start Page
- 736
- End Page
- 747
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/18570
- ISSN
- 1598-6446
- Abstract
- Simultaneous Localization and Map Building (SLAM) is one of the fundamental problems in robot navigation. The Extended Kalman Filter (EKF), which is widely adopted in SLAM approaches, requires extensive computation. The conventional particle filter also needs intense computation to cover a high dimensional state space with particles. This paper proposes an efficient SLAM method based on the recursive unscented Kalman filtering in an environment including a large number of landmarks. The posterior probability distributions of the robot pose and the landmark locations are represented by their marginal Gaussian probability distributions. In particular, the posterior probability distribution of the robot pose is calculated recursively. Each landmark location is updated with the recursively updated robot pose. The proposed method reduces filtering dimensions and computational complexity significantly, and has produced very encouraging results for navigation experiments with noisy multiple simultaneous observations.
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