Mitch Bryson

Building a Robust Implementation of Bearing-only Inertial SLAM for a UAV

This paper presents the on-going design and implementation of a robust inertial sensor based simultaneous localization and mapping (SLAM) algorithm for an unmanned aerial vehicle (UAV) using bearing-only observations. A single color vision camera is used to observe the terrain from which image points corresponding to features in the environment are extracted. The SLAM algorithm estimates the complete six degrees-of-freedom motion of the UAV along with the three-dimensional position of the features in the environment. An extended Kalman filter approach is used where a technique of delayed initialization is performed to initialize the three-dimensional position of features from bearing-only observations. Data association is achieved using a multihypothesis innovation gate based on the spatial uncertainty of each feature. Results are presented by running the algorithm off-line using inertial sensor and vision data collected during a flight test of a UAV.

Kite Aerial Photography for Low-Cost, Ultra-high Spatial Resolution Multi-Spectral Mapping of Intertidal Landscapes

Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging) provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae) and animal (e.g. gastropods) assemblages at multiple spatial and temporal scales.

Decentralised cooperative localisation for heterogeneous teams of mobile robots

This paper presents a distributed algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Inter-robot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable. In our proposed formulation, high frequency egocentric data (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server is easily duplicated for full redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

Airborne smoothing and mapping using vision and inertial sensors

This paper presents a framework for integrating sensor information from an Inertial Measuring Unit (IMU), Global Positioning System (GPS) receiver and monocular vision camera mounted to a low-flying Unmanned Aerial Vehicle (UAV) for building large-scale terrain reconstructions. Our method seeks to integrate all of the sensor information using a statistically optimal non-linear least squares smoothing algorithm to estimate vehicle poses simultaneously to a dense point feature map of the terrain. A visualisation of the terrain structure is then created by building a textured mesh-surface from the estimated point features. The resulting terrain reconstruction can be used for a range of environmental monitoring missions such as invasive plant detection and biomass mapping.

On the Observability of Bearing-only SLAM

In this paper we present an observability analysis for a mobile robot performing SLAM with a single monocular camera. The aim is to get a better understanding of the well known intuitive behavior of these systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates. The characterisation of the unobservable directions is made using the nullspace basis of the stripped observability matrix. This allows us to identify which vehicle motions are required to maximise the number of observable states in the system, which in turn affects accuracy in the estimation process. The analysis is performed by modelling the system in the continuous time domain as piecewise constant. Simulation results using an extended information filter are shown to verify the results of the observability analysis.

A Rotary-wing Unmanned Air Vehicle for Aquatic Weed Surveillance and Management

This paper addresses the novel application of an autonomous rotary-wing unmanned air vehicle (RUAV) as a cost-effective tool for the surveillance and management of aquatic weeds. A conservative estimate of the annual loss of agricultural revenue to the Australian economy due to weeds is in the order of A

Active airborne localisation and exploration in unknown environments using inertial SLAM

4 billion, hence the reason why weed control is of national significance. The presented system locates and identifies weeds in inaccessible locations. The RUAV is equipped with low-cost sensor suites and various weed detection algorithms. In order to provide the weed control operators with the capability of autonomous or remote control spraying and treatment of the aquatic weeds the RUAV is also fitted with a spray mechanism. The system has been demonstrated over inaccessible weed infested aquatic habitats.

Quantifying the response of structural complexity and community composition to environmental change in marine communities

Future unmanned aerial vehicle (UAV) applications will require high-accuracy localisation in environments in which navigation infrastructure such as the Global Positioning System (GPS) and prior terrain maps may be unavailable or unreliable. In these applications, long-term operation requires the vehicle to build up a spatial map of the environment while simultaneously localising itself within the map, a task known as simultaneous localisation and mapping (SLAM). In the first part of this paper we present an architecture for performing inertial-sensor based SLAM on an aerial vehicle. We demonstrate an on-line path planning scheme that intelligently plans the vehicles trajectory while exploring unknown terrain in order to maximise the quality of both the resulting SLAM map and localisation estimates necessary for the autonomous control of the UAV. Two important performance properties and their relationship to the dynamic motion and path planning systems on-board the UAV are analysed. Firstly we analyse information-based measures such as entropy. Secondly we perform an observability analysis of inertial SLAM by recasting the algorithms into an indirect error model form. Qualitative knowledge gained from the observability analysis is used to assist in the design of an information-based trajectory planner for the UAV. Results of the online path planning algorithm are presented using a high-fidelity 6-DoF simulation of a UAV during a simulated navigation and mapping task

Architectures for Cooperative Airborne Simultaneous Localisation and Mapping

Habitat structural complexity is a key factor shaping marine communities. However, accurate methods for quantifying structural complexity underwater are currently lacking. Loss of structural complexity is linked to ecosystem declines in biodiversity and resilience. We developed new methods using underwater stereo-imagery spanning 4 years (2010-2013) to reconstruct 3D models of coral reef areas and quantified both structural complexity at two spatial resolutions (2.5 and 25 cm) and benthic community composition to characterize changes after an unprecedented thermal anomaly on the west coast of Australia in 2011. Structural complexity increased at both resolutions in quadrats (4 m(2)) that bleached, but not those that did not bleach. Changes in complexity were driven by species-specific responses to warming, highlighting the importance of identifying small-scale dynamics to disentangle ecological responses to disturbance. We demonstrate an effective, repeatable method for quantifying the relationship among community composition, structural complexity and ocean warming, improving predictions of the response of marine ecosystems to environmental change.

True Color Correction of Autonomous Underwater Vehicle Imagery

This paper develops active Simultaneous Localisation And Mapping (SLAM) trajectory control strategies for multiple cooperating Unmanned Aerial Vehicles (UAVs) for tasks such as surveillance and picture compilation in Global Positioning System (GPS)-denied environments. Each UAV in the team uses inertial sensor and terrain sensor information to simultaneously localise the UAV while building a point feature map of the surrounding terrain, where map information is shared between vehicles over a data fusion network. Multi-vehicle active SLAM control architectures are proposed that actively plan the trajectories and motions of each of the vehicles in the team based on maximising information in the localisation and mapping estimates. We demonstrate and compare an ideal, centralised architecture, where a central planning node chooses optimal actions for each UAV, and a coordinated, decentralised architecture, where UAVs make their own control decisions based on common shared map information. The different architectures involve varying degrees of complexity and optimality through differing communications and computational requirements. Results are presented using a three-UAV team in a six-degree of freedom multi-UAV simulator.