A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach

A method to convert floating to fixed-point EKF-SLAM for embedded robotics

The Extended Kalman Filter (EKF) is one of the most efficient algorithms to address the problem of Simultaneous Localization And Mapping (SLAM) in the area of autonomous mobile robots. The EKF simultaneously estimates a model of the environment (map) and the position of a robot based on sensor information. The EKF for SLAM is usually implemented using floating-point data representation demanding high computational processing power, mainly when the processing is performed online during the environment exploration. In this paper, we propose a method to automatically estimate the bit-range of the EKF variables to mitigate its implementation using only fixed-point representation. In this method is presented a model to monitor the algorithm stability, a procedure to compute the bit range of each variable and a first effort to analyze the maximum acceptable system error. The proposed system can be applied to reduce the overall system cost and power consumption, specially in SLAM applications for embedded mobile robots

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available