On the consistency of EKF-SLAM: Focusing on the observation models

Abstract

Abstract-In this paper a new strategy for handling the observation information of a bearing-range sensor throughout the filtering process of EKF-SLAM is proposed. This new strategy is advised based on a thorough consistency analysis and aims to improve the process consistency while reducing the computational cost. At first, three different possible observation models are introduced for the EKF-SLAM solution for a robot equipped with a bearing-range sensor. General form of the covariance matrix and the level of inconsistency in the robot orientation estimate is then calculated for these variants, and based on the numerical comparison of the estimation results, it is proposed to use the bearing and range information of a feature in the initialization step of EKF-SLAM. However, it is recommended to use only the bearing information to perform other iteration steps. The simulation observations verify that the new strategy yields to more consistent estimates both for the robot and the features. Moreover, through the proposed consistency analysis, it is shown that since the source of consistency improvement is independent from the choice of the motion model, it gives us an advantage over other existing methods that assume a specific motion models for consistency improvement.

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Laser-Based Trespassing Prediction in Restrictive Environments: A Linear Approach

“…Range laser sensors are also used for target tracking applications, such as the case shown in [ 14 ], where a range laser sensor is used for environment modeling when applying a SLAM (Simultaneous Localization and Mapping) algorithm. A SLAM algorithm is used in mobile robot applications [ 3 , 4 , 15 – 18 ] to concurrently estimate the robot's position within an environment and to build a model of such an environment.…”

Analysis of Different Feature Selection Criteria Based on a Covariance Convergence Perspective for a SLAM Algorithm

“…Several algorithms have been proposed as solutions to the SLAM problem. The most widely used by the scientific community is the Extended Kalman filter (EKF) [ 1 , 3 – 6 ] solution and its derived filters, such as the Unscented Kalman filter (UKF) [ 3 ] and the Extended Information filter (EIF) [ 7 , 8 ]. In these filters, the SLAM system state, composed by the robot’s pose and the map of the environment, it is modeled as a Gaussian random variable.…”