Featureless vehicle-based visual SLAM with a consumer camera

The Simultaneous Localisation And Mapping (SLAM) problem is one of the major challenges in mobile robotics. Probabilistic techniques using high-end range finding devices are well established in the field, but recent work has investigated vision-only approaches. We present an alternative approach to the leading existing techniques, which extracts approximate rotational and translation velocity information from a vehicle-mounted consumer camera, without tracking landmarks. When coupled with an existing SLAM system, the vision module is able to map a 45 metre long indoor loop and a 1.6 km long outdoor road loop, without any parameter or system adjustment between tests. The work serves as a promising pilot study into ground-based vision-only SLAM, with minimal geometric interpretation of the environment.

Correlating vehicle forces and track profile

A research project was conducted at Queensland University of Technology on the relationship between the forces at the wheel-rail interface in track and the rate of degradation of track. Data for the study was obtained from an instrumented vehicle which ran repeatedly over a section of Queensland Rail's track in Central Queensland over a 6-month period. The wheel-rail forces had to be correlated with the elements of roughness in the test track profile, which were measured with a variety of equipment. At low frequencies, there was strong correlation between forces and profile, as expected, but diminishing correlation as frequencies increased.

Enhancing the experience of public transport users with urban screens and mobile applications

Public transportation is an environment with great potential for applying location-based services through mobile devices. This paper provides the underpinning rationale for research that will be looking at how the real-time passenger information system deployed by the Translink Transit Authority across all of South East Queensland in Australia can provide a core platform to improve commuters’ user experiences. This system relies on mobile computing and GPS technology to provide accurate information on transport vehicle locations. The proposal builds on this platform to inform the design and development of innovative social media, mobile computing and geospatial information applications. The core aim is to digitally augment the public transport environment to enhance the user experience of commuters for a more enjoyable journey.

Modelling urban public transit users’ route choice behaviour : a review and outlook

Public transport is one of the key promoters of sustainable urban transport. To encourage and increase public transport patronage it is important to investigate the route choice behaviours of urban public transit users. This chapter reviews the main developments of modelling urban public transit users’ route choice behaviours in a historical perspective, from the 1960s to the present time. The approaches re- viewed for this study include the early heuristic studies on finding the least-cost transit route and all-or- nothing transit assignment, the bus common lines problem, the disaggregate discrete choice models, the deterministic and stochastic user equilibrium transit assignment models, and the recent dynamic transit assignment models. This chapter also provides an outlook for the future directions of modelling transit users’ route choice behaviours. Through the comparison with the development of models for motorists’ route choice and traffic assignment problems, this chapter advocates that transit route choice research should draw inspiration from the research outcomes from the road area, and that the modelling practice of transit users’ route choice should further explore the behavioural complexities.

Dynamic analysis of on-board mass data to determine tampering in heavy vehicle on-board mass systems

Transport Certification Australia Limited, jointly with the National Transport Commission, has undertaken a project to investigate the feasibility of on-board mass monitoring (OBM) devices for regulatory purposes. OBM increases jurisdictional confidence in operational heavy vehicle compliance. This paper covers technical issues regarding potential use of dynamic data from OBM systems to indicate that tampering has occurred. Tamper-evidence and accuracy of current OBM systems needed to be determined before any regulatory schemes were put in place for its use. Tests performed to determine potential for, and ease of, tampering. An algorithm was developed to detect tamper events. Its results are detailed.

Static accuracy and precision of heavy vehicle on-board mass systems

Objective • Feasibility programme for on-board mass (OBM) monitoring of heavy vehicles (HVs) • Australian road authorities through Transport Certification Australia (TCA) • Accuracy of contemporary, commercially-available OBM units in Australia • Results need to be addressed/incorporated into specifications for Stage 2 of Intelligent Access Program (IAP) by Transport Certification Australia

Vision-based navigational competencies for a car-like vehicle

If mobile robots are to perform useful tasks in the real-world they will require a catalog of fundamental navigation competencies and a means to select between them. In this paper we describe our work on strongly vision-based competencies: road-following, person or vehicle following, pose and position stabilization. Results from experiments on an outdoor autonomous tractor, a car-like vehicle, are presented.

A cable-array robot for air vehicle simulation

The development of autonomous air vehicles can be an expensive research pursuit. To alleviate some of the financial burden of this process, we have constructed a system consisting of four winches each attached to a central pod (the simulated air vehicle) via cables - a cable-array robot. The system is capable of precisely controlling the three dimensional position of the pod allowing effective testing of sensing and control strategies before experimentation on a free-flying vehicle. In this paper, we present a brief overview of the system and provide a practical control strategy for such a system.

In the Rough : In-Field evaluation of an autonomous vehicle for golf course maintenance

Maintenance is a time consuming and expensive task for any golf course or driving range manager. For a golf course the primary tasks are grass mowing and maintenance (fertilizer and herbicide spreading), while for a driving range mowing, maintenance and ball collection are required. All these tasks require an operator to drive a vehicle along paths which are generally predefined. This paper presents some preliminary in-field tsting results for an automated tractor vehicle performing golf ball collection on an actual driving range, and mowing on difficult unstructured terrain.

Robust pose estimation for a car-like vehicle

In this paper, we outline the sensing system used for the visual pose control of our experimental car-like vehicle, the autonomous tractor. The sensing system consists of a magnetic compass, an omnidirectional camera and a low-resolution odometry system. In this work, information from these sensors is fused using complementary filters. Complementary filters provide a means of fusing information from sensors with different characteristics in order to produce a more reliable estimate of the desired variable. Here, the range and bearing of landmarks observed by the vision system are fused with odometry information and a vehicle model, providing a more reliable estimate of these states. We also present a method of combining a compass sensor with odometry and a vehicle model to improve the heading estimate.

Visual servoing of a car-like vehicle - an application of omnidirectional vision

In this paper, we develop the switching controller presented by Lee et al. for the pose control of a car-like vehicle, to allow the use of an omnidirectional vision sensor. To this end we incorporate an extension to a hypothesis on the navigation behaviour of the desert ant, cataglyphis bicolor, which leads to a correspondence free landmark based vision technique. The method we present allows positioning to a learnt location based on feature bearing angle and range discrepancies between the robot's current view of the environment, and that at a learnt location. We present simulations and experimental results, the latter obtained using our outdoor mobile platform.

The Green Falcon - Solar Powered Unmanned Aerial Vehicle

In recent months the extremes of Australia’s weather have affected, killed a good number of people and millions of dollars lost. Contrary to a manned aircraft or a helicopter; which have restricted air time, a UAS or a group of UAS could provide 24 hours coverage of the disaster area and be instrumented with infrared cameras to locate distressed people and relay information to emergency services. The solar powered UAV is capable of carrying a 0.25Kg payload consuming 0.5 watt and fly continuously for at low altitude for 24 hrs ,collect the data and create a special distribution . This system, named Green Falcon, is fully autonomous in navigation and power generation, equipped with solar cells covering its wing, it retrieves energy from the sun in order to supply power to the propulsion system and the control electronics, and charge the battery with the surplus of energy. During the night, the only energy available comes from the battery, which discharges slowly until the next morning when a new cycle starts. The prototype airplane was exhibited at the Melbourne Museum form Nov09 to Feb 2010.

The risky behaviour of young drivers : developing a measurement tool

The contribution of risky behaviour to the increased crash and fatality rates of young novice drivers is recognised in the road safety literature around the world. Exploring such risky driver behaviour has led to the development of tools like the Driver Behaviour Questionnaire (DBQ) to examine driving violations, errors, and lapses [1]. Whilst the DBQ has been utilised in young novice driver research, some items within this tool seem specifically designed for the older, more experienced driver, whilst others appear to asses both behaviour and related motives. The current study was prompted by the need for a risky behaviour measurement tool that can be utilised with young drivers with a provisional driving licence. Sixty-three items exploring young driver risky behaviour developed from the road safety literature were incorporated into an online survey. These items assessed driver, passenger, journey, car and crash-related issues. A sample of 476 drivers aged 17-25 years (M = 19, SD = 1.59 years) with a provisional driving licence and matched for age, gender, and education were drawn from a state-wide sample of 761 young drivers who completed the survey. Factor analysis based upon a principal components extraction of factors was followed by an oblique rotation to investigate the underlying dimensions to young novice driver risky behaviour. A five factor solution comprising 44 items was identified, accounting for 55% of the variance in young driver risky behaviour. Factor 1 accounted for 32.5% of the variance and appeared to measure driving violations that were transient in nature - risky behaviours that followed risky decisions that occurred during the journey (e.g., speeding). Factor 2 accounted for 10.0% of variance and appeared to measure driving violations that were fixed in nature; the risky decisions being undertaken before the journey (e.g., drink driving). Factor 3 accounted for 5.4% of variance and appeared to measure misjudgment (e.g., misjudged speed of oncoming vehicle). Factor 4 accounted for 4.3% of variance and appeared to measure risky driving exposure (e.g., driving at night with friends as passengers). Factor 5 accounted for 2.8% of variance and appeared to measure driver emotions or mood (e.g., anger). Given that the aim of the study was to create a research tool, the factors informed the development of five subscales and one composite scale. The composite scale had a very high internal consistency measure (Cronbach’s alpha) of .947. Self-reported data relating to police-detected driving offences, their crash involvement, and their intentions to break road rules within the next year were also collected. While the composite scale was only weakly correlated with self-reported crashes (r = .16, p < .001), it was moderately correlated with offences (r = .26, p < .001), and highly correlated with their intentions to break the road rules (r = .57, p < .001). Further application of the developed scale is needed to confirm the factor structure within other samples of young drivers both in Australia and in other countries. In addition, future research could explore the applicability of the scale for investigating the behaviour of other types of drivers.