Extended Kalman Filter – Simultaneous Localisation and Mapping (original) (raw)

LIMITS TO THE CONSISTENCY OF EKF-BASED SLAM 1

This paper analyzes the consistency of the classical extended Kalman filter (EKF) solution to the simultaneous localization and map building (SLAM) problem. Our results show that in large environments the map quickly becomes inconsistent due to linearization errors. We propose a new EKF-based SLAM algorithm, robocentric mapping, that greatly reduces linearization errors, improving map consistency. We also present results showing that large-scale mapping methods based on building local maps with a local uncertainty representation have better consistency than methods that work with global uncertainties.

Consistency of the EKF-SLAM Algorithm

2006

This paper presents a simulation-based analysis of the extended Kalman filter formulation of simultaneous localisation and mapping (EKF-SLAM). We show that the algorithm will always produce very inconsistent estimates once the "true" uncertainty in vehicle heading exceeds a limit. This failure is subtle and cannot, in general, be detected without ground-truth, although the inconsistent filter may exhibit observable symptoms, such as "jumpy" vehicle path. Conventional solutions-adding stabilising noise, using iterated or unscented filters, etc-do not improve the situation. However, if "small" heading uncertainty is maintained, EKF-SLAM behaves consistently and is insensitive to system noise and non-linearities. This result indicates the efficacy of submap methods for large-scale maps.

Limits to the consistency of EKF-based SLAM

IFAC Proceedings Volumes, 2004

This paper analyzes the consistency of the classical extended Kalman filter (EKF) solution to the simultaneous localization and map building (SLAM) problem. Our results show that in large environments the map quickly becomes inconsistent due to linearization errors. We propose a new EKF-based SLAM algorithm, robocentric mapping, that greatly reduces linearization errors, improving map consistency. We also present results showing that large-scale mapping methods based on building local maps with a local uncertainty representation (Tardós et al., 2002) have better consistency than methods that work with global uncertainties.

Convergence analysis for extended Kalman filter based SLAM

Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006

The main contribution of this paper is a theoretical analysis of the Extended Kalman Filter (EKF) based solution to the simultaneous localisation and mapping (SLAM) problem. The convergence properties for the general nonlinear two-dimensional SLAM are provided. The proofs clearly show that the robot orientation error has a significant effect on the limit and/or the lower bound of the uncertainty of the landmark location estimates. Furthermore, some insights to the performance of EKF SLAM and a theoretical analysis on the inconsistencies in EKF SLAM that have been recently observed are given.

Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM

IEEE Transactions on Robotics, 2000

This paper investigates the convergence properties and consistency of Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a rangeand-bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided.

New framework for Simultaneous Localization and Mapping: Multi map SLAM

International Conference on Robotics and Automation, 2008

The main contribution of this paper arises from the development of a new framework, which has its inspiration in the mechanics of human navigation, for solving the problem of Simultaneous Localization and Mapping (SLAM). The proposed framework has specific relevance to vision based SLAM, in particular, small baseline stereo vision based SLAM and addresses several key issues relevant to the particular sensor domain. Firstly, as observed in the authors' earlier work, the particular sensing device has a highly nonlinear observation model resulting in inconsistent state estimations when standard recursive estimators such as the Extended Kalman Filter (EKF) or the Unscented variants are used.

Comparison of EKF based SLAM and optimization based SLAM algorithms

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2018

This paper compares the recent developed state-of-the-art extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithm, namely, invariant EKF SLAM, with the nonlinear least squares optimization based SLAM algorithms. Simulations in 1D, 2D, and 3D are used to evaluate the invariant EKF SLAM algorithm. It is demonstrated that in most 2D/3D scenarios with practical noise levels, the accuracy of invariant EKF is very close to that of nonlinear least squares optimization based SLAM. In the simple 1D case, the Kalman filter results and the linear least squares results are exactly the same (for any noise levels) due to the linear motion model and linear observation model involved.

Realtime autonomous navigation in V-Rep based static and dynamic environment using EKF-SLAM

IAES International Journal of Robotics and Automation (IJRA), 2021

Localization in an autonomous mobile robot allows it to operate autonomously in an unknown and unpredictable environment with the ability to determine its position and heading. Simultaneous localization and mapping (SLAM) are introduced to solve the problem where no prior information about the environment is available either static or dynamic to achieve standard map-based localization. The primary focus of this research is autonomous mobile robot navigation using the extended Kalman filter (EKF)-SLAM environment modeling technique which provides higher accuracy and reliability in mobile robot localization and mapping results. In this paper, EKF-SLAM performance is verified by simulations performed in a static and dynamic environment designed in V-REP i.e., 3D Robot simulation environment. In this work SLAM problem of two-wheeled differential drive robot i.e., Pioneer 3-DX in indoor static and dynamic environment integrated with Laser range finder i.e., Hokuyo URG-04LX-UG01, LIDAR, and Ultrasonic sensors is solved. EKF-SLAM scripts are developed using MATLAB that is linked to V-REP via remote API feature to evaluate EKF-SLAM performance. The reached results confirm the EKF-SLAM is a reliable approach for real-time autonomous navigation for mobile robots in comparison to other techniques.