TY - GEN
T1 - Computationally efficient algorithm for simultaneous localization and mapping (SLAM)
AU - Yang, Cheng Kai
AU - Hsu, Chen Chien
AU - Wang, Yin Tien
PY - 2013
Y1 - 2013
N2 - FastSLAM is a popular method to solve the problem of simultaneous localization and mapping. However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in particles. As a result, the execution speed would be too slow to achieve the objective of real-time design. As an attempt to solve this problem, this paper presents an enhanced architecture for FastSLAM called computationally efficient SLAM (CESLAM), where odometer information is considered for updating the robot's pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates. Simulation results show that the proposed algorithm in this paper can overcome the problem of the time-consuming process due to unnecessary comparisons and improve the accuracy of localization and mapping.
AB - FastSLAM is a popular method to solve the problem of simultaneous localization and mapping. However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in particles. As a result, the execution speed would be too slow to achieve the objective of real-time design. As an attempt to solve this problem, this paper presents an enhanced architecture for FastSLAM called computationally efficient SLAM (CESLAM), where odometer information is considered for updating the robot's pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates. Simulation results show that the proposed algorithm in this paper can overcome the problem of the time-consuming process due to unnecessary comparisons and improve the accuracy of localization and mapping.
KW - Extended Kalman Filter
KW - FastSLAM
KW - Particle Filter
UR - http://www.scopus.com/inward/record.url?scp=84881293977&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881293977&partnerID=8YFLogxK
U2 - 10.1109/ICNSC.2013.6548759
DO - 10.1109/ICNSC.2013.6548759
M3 - Conference contribution
AN - SCOPUS:84881293977
SN - 9781467351980
T3 - 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
SP - 328
EP - 332
BT - 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
T2 - 2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013
Y2 - 10 April 2013 through 12 April 2013
ER -