This paper addresses the problem of implementing simultaneous localisation and map building (SLAM) in very large outdoor environments. A method is presented to reduce the computational requirement from ~O(N) to ~O(N), being N the states used to represent all the landmarks and vehicle pose. With this implementation the memory requirement are also reduced to ~O(N). This algorithm presents an effi...

Sensor localization is a central problem for sensor networks. If the sensor positions are uncertain, the target tracking ability of the sensor network is reduced. Sensor localization in underwater environments is traditionally addressed using acoustic range measurements involving known anchor or surface nodes. We explore the usage of triaxial magnetometers and a friendly vessel with known magne...

A respiratory chamber is used for monitoring VO2, VCO2 and RQ in humans, enabling long term (24h) observation under free living conditions. Computation of VO2 and VCO2 is currently done by inversion of a mass balance equation, with no consideration of measurement errors and other uncertainties. In order to improve the accuracy of the results, a new mathematical model is suggested in the present...

Report [1] compares the Extended Kalman Filter [2, 3, 4], the Iterated Extended Kalman Filter, IEKF, [2, 3, 4] and the Linear Regression Kalman Filter [5] (e.g. the Unscented Kalman Filter, UKF, [6, 7, 8]) on (i) consistency and (ii) information content of their results (estimates and covariance matrices). The nonlinear filter proposed by Bellaire et al. in [9] is not discussed in report [1]. T...

Abstract The purpose of this review is to present a comprehensive overview the theory ensemble Kalman–Bucy filtering for continuous-time, linear-Gaussian signal and observation models. We system equations that describe flow individual particles sample covariance mean in continuous-time filtering. consider these their characteristics number popular Kalman variants. Given equations, we study asym...

In this paper, we consider the development of unbiased estimators for ensemble Kalman-Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology, which can be viewed as analog famous discrete-time Kalman filter. Our will motivated from recent work (Rhee and Glynn, Oper. Res., 63:1026-1053, 2015) introduces randomization means to produce finite variance estimators. enters through ...

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 ...

Obviously navigation is one of the most complicated issues in mobile robots. Intelligent algorithms are often used for error handling in robot navigation. This Paper deals with the problem of Inertial Measurement Unit (IMU) error handling by using Extended Kalman Filter (EKF) as an Expert Algorithms. Our focus is put on the field of mobile robot navigation in the 2D environments. The main chall...