Invariant EKF Design for Scan Matching-aided Localization

Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design

QuantSat-PT: an attitude determination and control system architecture for QKD

This article presents the QuantSaT-PT project, an effort to create the first Portuguese nanosatellite for space to ground quantum communication. Focused on the Attitude Determination and Control System, it describes the different elements that allow for the attainment of diverse accuracy levels required for separate mission stages. Given the harsh pointing precision necessary for establishing a quantum downlink, the implementation of this module presents a major challenge in the Cubesat field. Furthermore, the introduced architecture aims to reduce system cost by replacing the state-of-the-art star tracker with ground beacon detection

Fusion of Diverse Performance Inertial Sensors for Improved Attitude Estimation Within a Stabilisation Platform for Electro-optic Systems

Within line of sight pointing and stabilisation of EO (Electro-optic) systems operating under motion disturbances it is desirable to measure the inertial orientation of different parts of the system, not just the line of sight - this would allow additional information to be added to the control loop. To implement this a framework to fuse the multiple inertial sensors of the EO system is considered, with an example implemented. The fusion of higher performance sensors located at the line of sight is implemented within the proposed framework, to improve the performance of the estimate at the location of the lower performance sensor. The fusion framework makes use of cascaded Multiplicative Extended Kalman Filter that estimate the multiplicative error of the quaternion orientation estimate

Lie Algebraic Unscented Kalman Filter for Pose Estimation

An unscented Kalman filter for matrix Lie groups is proposed where the time propagation of the state is formulated on the Lie algebra. This is done with the kinematic differential equation of the logarithm, where the inverse of the right Jacobian is used. The sigma points can then be expressed as logarithms in vector form, and time propagation of the sigma points and the computation of the mean and the covariance can be done on the Lie algebra. The resulting formulation is to a large extent based on logarithms in vector form, and is therefore closer to the UKF for systems in $\mathbb{R}^n$. This gives an elegant and well-structured formulation which provides additional insight into the problem, and which is computationally efficient. The proposed method is in particular formulated and investigated on the matrix Lie group $SE(3)$. A discussion on right and left Jacobians is included, and a novel closed form solution for the inverse of the right Jacobian on $SE(3)$ is derived, which gives a compact representation involving fewer matrix operations. The proposed method is validated in simulations

Rigid Body Attitude Estimation: An Overview and Comparative Study

The attitude estimation of rigid body systems has attracted the attention of many researchers over the years. The development of efficient estimation algorithms that can accurately estimate the orientation of a rigid body is a crucial step towards a reliable implementation of control schemes for underwater and flying vehicles. The primary focus of this thesis consists in investigating various attitude estimation techniques and their applications. Two major classes are discussed. The first class consists of the earliest static attitude determination techniques relying solely on a set of body vector measurements of known vectors in the inertial frame. The second class consists of dynamic attitude estimation and filtering techniques, relying on body vector measurements as well other measurements, and using the dynamical equations of the system under consideration. Various attitude estimation algorithms, including the latest nonlinear attitude observers, are presented and discussed, providing a survey that covers the evolution and structural differences of these estimation methods. Simulation results have been carried out for a selected number of such attitude estimators. Their performance in the presence of noisy measurements, as well as their advantages and disadvantages are discussed