3 research outputs found
Fast Adaptation Nonlinear Observer for SLAM
The process of simultaneously mapping the environment in three dimensional
(3D) space and localizing a moving vehicle's pose (orientation and position) is
termed Simultaneous Localization and Mapping (SLAM). SLAM is a core task in
robotics applications. In the SLAM problem, each of the vehicle's pose and the
environment are assumed to be completely unknown. This paper takes the
conventional SLAM design as a basis and proposes a novel approach that ensures
fast adaptation of the nonlinear observer for SLAM. Due to the fact that the
true SLAM problem is nonlinear and is modeled on the Lie group of
, the proposed observer for SLAM is nonlinear
and modeled on . The proposed observer
compensates for unknown bias attached to velocity measurements. The results of
the simulation illustrate the robustness of the proposed approach.Comment: 2020 IEEE 24th International Conference on System Theory, Control and
Computing (ICSTCC
Guaranteed Performance Nonlinear Observer for Simultaneous Localization and Mapping
A geometric nonlinear observer algorithm for Simultaneous Localization and
Mapping (SLAM) developed on the Lie group of \mathbb{SLAM}_{n}\left(3\right) is
proposed. The presented novel solution estimates the vehicle's pose (i.e.
attitude and position) with respect to landmarks simultaneously positioning the
reference features in the global frame. The proposed estimator on manifold is
characterized by predefined measures of transient and steady-state performance.
Dynamically reducing boundaries guide the error function of the system to
reduce asymptotically to the origin from its starting position within a large
given set. The proposed observer has the ability to use the available velocity
and feature measurements directly. Also, it compensates for unknown constant
bias attached to velocity measurements. Unit-qauternion of the proposed
observer is presented. Numerical results reveal effectiveness of the proposed
observer. Keywords: Nonlinear filter algorithm, Nonlinear observer for
Simultaneous Localization and Mapping, Nonlinear estimator, nonlinear SLAM
observer on manifold, nonlinear SLAM filter on matrix Lie Group, observer
design, asymptotic stability, systematic convergence, Prescribed performance
function, pose estimation, attitude filter, position filter, feature filter,
landmark filter, gradient based SLAM observer, gradient based observer for
SLAM, adaptive estimate, SLAM observer, observer SLAM framework, equivariant
observer, inertial vision unit, visual, SLAM filter, SE(3), SO(3)
Landmark and IMU Data Fusion: Systematic Convergence Geometric Nonlinear Observer for SLAM and Velocity Bias
Navigation solutions suitable for cases when both autonomous robot's pose
(\textit{i.e}., attitude and position) and its environment are unknown are in
great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need
by concurrently mapping the environment and observing robot's pose with respect
to the map. This work proposes a nonlinear observer for SLAM posed on the
manifold of the Lie group of , characterized
by systematic convergence, and designed to mimic the nonlinear motion dynamics
of the true SLAM problem. The system error is constrained to start within a
known large set and decay systematically to settle within a known small set.
The proposed estimator is guaranteed to achieve predefined transient and
steady-state performance and eliminate the unknown bias inevitably present in
velocity measurements by directly using measurements of angular and
translational velocity, landmarks, and information collected by an inertial
measurement unit (IMU). Experimental results obtained by testing the proposed
solution on a real-world dataset collected by a quadrotor demonstrate the
observer's ability to estimate the six-degrees-of-freedom (6 DoF) robot pose
and to position unknown landmarks in three-dimensional (3D) space. Keywords:
Simultaneous Localization and Mapping, Nonlinear filter for SLAM, Nonlinear
filter for SLAM on Matrix Lie group, pose, asymptotic stability, prescribed
performance, adaptive estimate, feature, inertial measurement unit, inertial
vision unit, IMU, SE(3), SO(3), noise