Simultaneous Localization and Mapping Based on Kalman Filter and Extended Kalman Filter

Extended Kalman Filter Based Fuzzy Adaptive Filter

Simultaneous Localization and Mapping with Limited Sensing Using Extended Kalman Filter and Hough Transform

Original scientific paper The problem of a robot to create a map of an unknown environment while correcting its own position based on the same map and sensor data is called Simultaneous Localization and Mapping problem. As the accuracy and precision of the sensors have an important role in this problem, most of the proposed systems include the usage of high cost laser range sensors, and relativ...

The Particle Filter and Extended Kalman Filter methods for the structural system identification considering various uncertainties

Structural system identification using recursive methods has been a research direction of increasing interest in recent decades. The two prominent methods, including the Extended Kalman Filter (EKF) and the Particle Filter (PF), also known as the Sequential Monte Carlo (SMC), are advantageous in this field. In this study, the system identification of a shake table test of a 4-story steel struct...

Experiment on Simultaneous Localization and Mapping Based on Unscented Kalman Filter for Unmanned Underwater Vehicles

This paper proposes a simultaneous localization and mapping (SLAM) scheme applicable to the autonomous navigation of unmanned underwater vehicles (UUV). A SLAM scheme is an alternative navigation method for measuring the environment through which the vehicle is passing and providing the relative position of the unmanned vehicle. An unscented Kalman filter (UKF) is utilized in order to develop a...

Extended Kalman Filter Tutorial

In the following we assume that the random vector wk captures uncertainties in the model and vk denotes the measurement noise. Both are temporally uncorrelated (white noise), zero-mean random sequences with known covariances and both of them are uncorrelated with the initial state x0. E[wk] = 0 E[wkw T k ] = Qk E[wkw T j ] = 0 for k 6= j E[wkx T 0 ] = 0 for all k (3) E[vk] = 0 E[vkv T k ] = Rk ...