Particle Filter-Based SLAM from Localization Viewpoint

被引：1
|
作者：
Havangi, Ramazan [1 ]
机构：
[1] Univ Birjand, Fac Elect & Comp Engn, Birjand, Iran
来源：
INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS | 2016年 / 13卷 / 03期
关键词：
SLAM; particle filter; maximum likelihood; marginal extended particle filter; PARAMETER-ESTIMATION; FASTSLAM; STATE;
D O I：
10.1142/S0219843616500018
中图分类号：
TP24 [机器人技术];
学科分类号：
080202 ; 1405 ;
摘要：
In order to enhance consistency in simultaneous localization and mapping (SLAM), in this paper, this problem is considered as a solely localization problem in the presence of unknown parameters. In this approach, the proposal distribution is generated based on marginal extended particle filter and static map is considered as a parametric estimation that is estimated by maximum likelihood techniques. Significant improvement of the filtering result from this viewpoint is demonstrated in terms of estimation performance and consistency. Some simulations and experiments are presented to evaluate the algorithm's performance in comparison to conventional methods.
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收藏
页数：18
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