An Behavior-based Robotics
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Global Path Planning in Gaussian Probabilistic Maps
Journal of Intelligent and Robotic Systems
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation Approach to Simultaneous Localization and Map Building (Springer Tracts in Advanced Robotics)
Robocentric map joining: Improving the consistency of EKF-SLAM
Robotics and Autonomous Systems
Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection
Computers and Electronics in Agriculture
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This paper addresses the problem of a features selection criterion for a simultaneous localization and mapping (SLAM) algorithm implemented on a mobile robot. This SLAM algorithm is a sequential extended Kalman filter (EKF) implementation that extracts corners and lines from the environment. The selection procedure is made according to the convergence theorem of the EKF-based SLAM. Thus, only those features that contribute the most to the decreasing of the uncertainty ellipsoid volume of the SLAM system state will be chosen for the correction stage of the algorithm. The proposed features selection procedure restricts the number of features to be updated during the SLAM process, thus allowing real time implementations with non-reactive mobile robot navigation controllers. In addition, a Monte Carlo experiment is carried out in order to show the map reconstruction precision according to the Kullback–Leibler divergence curves. Consistency analysis of the proposed SLAM algorithm and experimental results in real environments are also shown in this work.