Near minimum time path planning for bearing-only localisation and mapping

The main contribution of this paper is an algorithm for integrating motion planning and simultaneous localisation and mapping (SLAM). Accuracy of the maps and the robot locations computed using SLAM is strongly dependent on the characteristics of the environment, for example feature density, as well as the speed and direction of motion of the robot. Appropriate control of the robot motion is particularly important in bearing-only SLAM, where the information from a moving sensor is essential. In this paper a near minimum time path planning algorithm with a finite planning horizon is proposed for bearing-only SLAM. The objective of the algorithm is to achieve a predefined mapping precision while maintaining acceptable vehicle location uncertainty in the minimum time. Simulation results have shown the effectiveness of the proposed method. © 2005 IEEE

On Foveated Gaze Control and Combined Gaze and Locomotion Planning

This chapter presents recent research results of our laboratory in the area of vision an

Fusion of Visible and Thermal-Infrared Imagery for SLAM for Landing on Icy Moons

This paper addresses the problem of localization for landing on the surface of icy moons, like Europa or Enceladus. Due to the possibility of specular reflection as well as high bulk albedo, icy surfaces present new challenges that make traditional vision-based navigation systems relying on visible imagery unreliable. We propose augmenting visible light cameras with a thermal-infrared camera using inverse-depth parameterized monocular EKF-SLAM to address problems arising from the appearance of icy moons. Results were obtained from a novel procedural Europa surface simulation which models the appearance and the thermal properties simultaneously from physically-based methods. In this framework, we show that thermal features improve localization by 23% on average when compared to a visible camera. Moreover, fusing both sensing modalities increases the improvement in localization to 31% on average, compared to using a visible light camera alone

A Daisy-Chaining Visual Servoing Approach with Applications in Tracking, Localization, and Mapping

Non

Gust disturbance alleviation with Incremental Nonlinear Dynamic Inversion

Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near obstacles by their ability to withstand wind gusts. Currently widespread position control methods such as Proportional Integral Derivative control do not perform well under the influence of gusts. Incremental Nonlinear Dynamic Inversion (INDI) is a sensor-based control technique that can control nonlinear systems subject to disturbances. This method was developed for the attitude control of MAVs, but in this paper we generalize this method to the outer loop control of MAVs under gust loads. Significant improvements over a traditional Proportional Integral Derivative (PID) controller are demonstrated in an experiment where the drone flies in and out of a fan's wake. The control method does not rely on frequent position updates, so it is ready to be applied outside with standard GPS modules

An Investigation of Nonlinear Estimation and System Design for Mechatronic Systems

This thesis is a collection of two projects in which the author was involved during his master\u27s degree program. The first project involves the estimation of 3D Euclidean coordinates of features from 2D images. A 3D Euclidean position estimation strategy is developed for a static object using a single moving camera whose motion is known. This Euclidean depth estimator has a very simple mathematical structure and is easy to implement. Numerical simulations and experimental results using a mobile robot in an indoor environment are presented to illustrate the performance of the algorithm. The second section describes the design of a test system for the Argon Environment Electrical Study (AEES) conducted by the Department of Energy (DOE). The initial research proposal, safety review, and literature review are presented. Additionally, the test plan and system design are highlighted

MonoSLAM: Real-time single camera SLAM

Published versio

UAV Parameter Estimation with Gaussian Process Approximations

Unmanned Aerial Vehicles (UAVs) provide an alternative to manned aircraft for risk associated missions and applications where sizing constraints require miniaturized flying platforms. UAVs are currently utilised in an array of applications ranging from civilian research to military battlegrounds. A part of the development process for UAVs includes constructing a flight model. This model can be used for modern flight controller design and to develop high fidelity flight simulators. Furthermore, it also has a role in analysing stability, control and handling qualities of the platform. Developing such a model involves estimating stability and control parameters from flight data. These map the platform's control inputs to its dynamic response. The modeling process is labor intensive and requires coarse approximations. Similarly, models constructed through flight tests are only applicable to a narrow flight envelope and classical system identification approaches require prior knowledge of the model structure, which, in some instances may only be partially known. This thesis attempts to find a solution to these problems by introducing a new system identification method based on dependent Gaussian processes. The new method would allow for high fidelity non-linear flight dynamic models to be constructed through experimental data. The work is divided into two main components. The first part entails the development of an algorithm that captures cross coupling between input parameters, and learns the system stability and control derivatives. The algorithm also captures any dependencies embodied in the outputs. The second part focuses on reducing the heavy computational cost, which is a deterrent to learning the model from large test flight data sets. In addition, it explores the capabilities of the model to capture any non-stationary behavior in the aerodynamic coefficients. A modeling technique was developed that uses an additive sparse model to combine global and local Gaussian processes to learn a multi-output system. Having a combined approximation makes the model suitable for all regions of the flight envelope. In an attempt to capture the global properties, a new sampling method is introduced to gather information about the output correlations. Local properties were captured using a non-stationary covariance function with KD-trees for neighbourhood selection. This makes the model scalable to learn from high dimensional large-scale data sets. The thesis provides both theoretical underpinnings and practical applications of this approach. The theory was tested in simulation on a highly coupled oblique wing aircraft and was demonstrated on a delta-wing UAV platform using real flight data. The results were compared against an alternative parametric model and demonstrated robustness, improved identification of coupling between flight modes, sound ability to provide uncertainty estimates, and potential to be applied to a broader flight envelope