Proceedings ArticleDOI
A SLAM algorithm based on the central difference Kalman filter
Jihua Zhu,Nanning Zheng,Zejian Yuan,Qiang Zhang,Xuetao Zhang,Yongjian He +5 more
- pp 123-128
TLDR
Sterling's polynomial interpolation method is employed to approximate nonlinear models and combined with the Kanlman filter framework, CDKF is proposed to solve the probabilistic state-space SLAM problem.Abstract:
This paper presents an central difference Kalman filter (CDKF) based Simultaneous Localization and Mapping (SLAM) algorithm, which is an alternative to the classical extended Kalman filter based SLAM solution (EKF-SLAM). EKF-SLAM suffers from two important problems, which are the calculation of Jacobians and the linear approximations to the nonlinear models. They can lead the filter to be inconsistent. To overcome the serious drawbacks of the previous frameworks, Sterling's polynomial interpolation method is employed to approximate nonlinear models. Combined with the Kanlman filter framework, CDKF is proposed to solve the probabilistic state-space SLAM problem. The proposed approach improves the filter consistency and state estimation accuracy. Both simulated experiments and bench mark data set are used to demonstrating the superiority of the proposed algorithm.read more
Citations
More filters
Journal ArticleDOI
Performance analysis of NDT-based graph SLAM for autonomous vehicle in diverse typical driving scenarios of Hong Kong
TL;DR: In this article, LiDAR-based graph SLAM was evaluated in diverse urban scenarios to further evaluate the relationship between the performance of Lidar-based SLAM and scenario conditions.
Journal ArticleDOI
EGP-CDKF for Performance Improvement of the SINS/GNSS Integrated System
TL;DR: Gaussian processes (GP)-based is presented to enhance the capabilities of prediction and estimation for parametric CDKF and shows that large performance benefits are achieved through applying the enhanced GP-CDKF(EGP-CDF) into the SINS/GNSS integrated system.
Journal ArticleDOI
Kalman Filter: Historical Overview and Review of Its Use in Robotics 60 Years after Its Creation
Claudio Urrea,Rayko Agramonte +1 more
TL;DR: In this paper, the authors reviewed some of the modifications conducted on the Kalman filter over the last few decades and compared the characteristics of each modification on this filter, including consistency, convergence, and accuracy.
Journal ArticleDOI
Monitoring of wastewater treatment plants using improved univariate statistical technique
TL;DR: This paper is to develop univariate statistical technique that aims at enhancing the monitoring of wastewater treatment plants using an improved particle filtering (IPF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA).
Journal ArticleDOI
A Strong Tracking Square Root Central Difference FastSLAM for Unmanned Intelligent Vehicle With Adaptive Partial Systematic Resampling
Dan Liu,Jianmin Duan,Hui Shi +2 more
TL;DR: An improved fast simultaneous localization and mapping (FastSLAM) algorithm based on the strong tracking square root central difference Kalman filter (STSRCDKF) with adaptive partial systematic resampling with better adaptability and robustness to respond with time-varying measurement noise is proposed to solve the large-scale SLAM problem for unmanned intelligent vehicle.
References
More filters
Journal ArticleDOI
Unscented filtering and nonlinear estimation
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
Journal ArticleDOI
Simultaneous localization and mapping: part I
TL;DR: This paper describes the simultaneous localization and mapping (SLAM) problem and the essential methods for solving the SLAM problem and summarizes key implementations and demonstrations of the method.
Journal ArticleDOI
A solution to the simultaneous localization and map building (SLAM) problem
TL;DR: The paper proves that a solution to the SLAM problem is indeed possible and discusses a number of key issues raised by the solution including suboptimal map-building algorithms and map management.
Journal ArticleDOI
Simultaneous localization and mapping (SLAM): part II
TL;DR: This paper discusses the recursive Bayesian formulation of the simultaneous localization and mapping (SLAM) problem in which probability distributions or estimates of absolute or relative locations of landmarks and vehicle pose are obtained.