TY - GEN
T1 - Simultaneous localization and mapping
T2 - 2008 4th International Conference on Information and Automation for Sustainability, ICIAFS 2008
AU - Pathiranage, Chandima Dedduwa
AU - Watanabe, Keigo
AU - Jayasekara, Buddhika
AU - Izumi, Kiyotaka
PY - 2008/12/1
Y1 - 2008/12/1
N2 - This paper describes an improved solution to the simultaneous localization and mapping (SLAM) problem based on pseudolinear models. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. A state estimator which can be designed to use the nonlinearity as it is coming from the original model has always been invaluable in which high accuracy is expected. Thus to accomplish the above highlighted point, pseudolinear model based Kalman filter (PLKF) state estimator is introduced. Evolution of vehicle motion is modeled using vehicle frame translation derived from successive dead reckoned poses as a control input. A pseudolinear process model is proposed to improve the accuracy and the faster convergence of state estimation. The general sensor model is presented in a pseudolinear form to preserve the nonlinearity in the observation model. The PLKF-based SLAM algorithm is simulated using Matlab for vehicle-landmarks system and results show that the proposed approach performs much accurately compared to the well known EKF-SLAM algorithm.
AB - This paper describes an improved solution to the simultaneous localization and mapping (SLAM) problem based on pseudolinear models. Accurate estimation of vehicle and landmark states is one of the key issues for successful mobile robot navigation if the configuration of the environment and initial robot location are unknown. A state estimator which can be designed to use the nonlinearity as it is coming from the original model has always been invaluable in which high accuracy is expected. Thus to accomplish the above highlighted point, pseudolinear model based Kalman filter (PLKF) state estimator is introduced. Evolution of vehicle motion is modeled using vehicle frame translation derived from successive dead reckoned poses as a control input. A pseudolinear process model is proposed to improve the accuracy and the faster convergence of state estimation. The general sensor model is presented in a pseudolinear form to preserve the nonlinearity in the observation model. The PLKF-based SLAM algorithm is simulated using Matlab for vehicle-landmarks system and results show that the proposed approach performs much accurately compared to the well known EKF-SLAM algorithm.
KW - Odometry measurement
KW - Pseudolinear kalman filter
KW - Pseudolinear model
KW - Simultaneous localization and mapping
UR - http://www.scopus.com/inward/record.url?scp=64049096562&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=64049096562&partnerID=8YFLogxK
U2 - 10.1109/ICIAFS.2008.4783921
DO - 10.1109/ICIAFS.2008.4783921
M3 - Conference contribution
AN - SCOPUS:64049096562
SN - 9781424429004
T3 - Proceedings of the 2008 4th International Conference on Information and Automation for Sustainability, ICIAFS 2008
SP - 61
EP - 66
BT - Proceedings of the 2008 4th International Conference on Information and Automation for Sustainability, ICIAFS 2008
Y2 - 12 December 2008 through 14 December 2008
ER -