Flexibility and robustness of visual SLAM systems have been shown to benefit from an inverse depth parameterisation of features. However the increased number of 6 parameters per feature presents a problem to real-time EKF SLAM implementations because their computational complexity scales quadratically with the size of the state vector. Recent work tackles this for instance by converting the rep...

A standard approach to tracking is to use the Extended Kalman Filter (EKF) applied to a non-linear state-space model. We will compare two conceivable choices of state variables for modeling civil aircrafts. One where Cartesian velocities are used and one where absolute velocity and heading angle are used. In both choices, Cartesian coordinates are used for position and angular velocity for turn...

The extended Kalman filter (EKF) and unscented Kalman filter (UKF) for nonlinear state estimation with both additive and nonadditive noise structures are presented and compared. Three different Global Positioning System (GPS)/inertial navigation system (INS) sensor fusion formulations for attitude estimation are used as case studies for the nonlinear state estimation problem. A diverse set of a...

For various target tracking applications, it is well known that the Kalman filter optimal estimator(in minimum mean-square sense) to predict and estimate state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In case nonlinear systems, Extended filter(EKF) Unscented filter(UKF) are widely used, which can be viewed as approximations the(linear) in sense ...

A new filter is proposed that achieves reliable state estimation in nonlinear systems with multiple equilibrium points. The latter may exhibit strong nonlinearity and sudden transient behavior triggered by either system process noise, or observation noise, or outliers. Our filter is able to output the correct qualitative state over time quickly and reliably when the system dynamics experience s...

A new spacecraft attitude estimation approach based on the Unscented Filter is derived. For nonlinear systems the Unscented Filter uses a carefully selected set of sample points to more accurately map the probability distribution than the linearization of the standard Extended Kalman Filter, leading to faster convergence from inaccurate initial conditions in attitude estimation problems. The fi...

Accurate position and velocity estimation of a re-entry vehicle is essential for realizing its deviation from the desired descent trajectory and providing necessary guidance command in real-time. Generally the Extended Kalman Filter (EKF) is utilized for position and velocity estimation of a space vehicle. However, in the EKF the error covariance is predicted by linearizing the non-linear dynam...

The Geometrically Intrinsic Nonlinear Recursive Filter, or GI Filter, is designed to estimate an arbitrary continuous-time Markov diffusion process X subject to nonlinear discrete-time observations. The GI Filter is fundamentally different from the much-used Extended Kalman Filter (EKF), and its secondorder variants, even in the simplest nonlinear case, in that: ¥ It uses a quadratic function o...

This paper proposes a novel approach to an RGB-D simultaneous localization and mapping (SLAM) system that uses the vanishing point and door plates in a corridor environment simultaneously for navigation. To increase the stability of the SLAM process in such an environment, the vanishing point and door plates are utilized as landmarks for extended Kalman filterbased SLAM (i.e., EKF SLAM). The va...