Title: Simultaneous localization and mapping based on particle filter for sparse environment
Abstract: This paper presents a method for solving simulation localization and mapping (SLAM) in sparse-feature environment, by adopting a concept of particle filter with multiple extended Kalman filters (EKF). Compared with common FastSLAM where each particle is a sample of one vehicle path whereas each EKF is solely a feature estimator, the proposed algorithm includes the vehicle-pose estimate in each EKF whereas the particle is a sample of vehicle motion. Thus, the proposed algorithm ensures dead reckoning in the absence of features. Map construction is based on line features which are extracted from observation of the environment. Finally, simulation results demonstrate the feasibility and performance of the proposed SLAM algorithm.
Publication Year: 2014
Publication Date: 2014-07-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 1
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot