Separation management approaches during periods of communication failure

New air traffic automated separation management concepts are constantly under investigation. Yet most of the automated separation management algorithms proposed over the last few decades have assumed either perfect communication or exact knowledge of all aircraft locations. In realistic environments, these idealized assumptions are not valid and any communication failure can potentially lead to disastrous outcomes. This paper examines the separation performance behavior of several popular algorithms during periods of information loss. This comparison is done through simulation studies. These simulation studies suggest that communication failure can cause the performance of these separation management algorithms to degrade significantly. This paper also describes some preliminary flight tests.

Choice of image features for depth-axis control in image based visual servo control

This paper is concerned with choosing image features for image based visual servo control and how this choice influences the closed-loop dynamics of the system. In prior work, image features tend to be chosen on the basis of image processing simplicity and noise sensitivity. In this paper we show that the choice of feature directly influences the closed-loop dynamics in task-space. We focus on the depth axis control of a visual servo system and compare analytically various approaches that have been reported recently in the literature. The theoretical predictions are verified by experiment.

Adaptive depth estimation in image based visual servo control of dynamic systems

This paper considers the question of designing a fully image based visual servo control for a dynamic system. The work is motivated by the ongoing development of image based visual servo control of small aerial robotic vehicles. The observed targets considered are coloured blobs on a flat surface to which the normal direction is known. The theoretical framework is directly applicable to the case of markings on a horizontal floor or landing field. The image features used are a first order spherical moment for position and an image flow measurement for velocity. A fully non-linear adaptive control design is provided that ensures global stability of the closed-loop system. © 2005 IEEE.

Omnidirectional visual odometry for a planetary rover

Position estimation for planetary rovers has been typically limited to odometry based on proprioceptive measurements such as the integration of distance traveled and measurement of heading change. Here we present and compare two methods of online visual odometry suited for planetary rovers. Both methods use omnidirectional imagery to estimate motion of the rover. One method is based on robust estimation of optical flow and subsequent integration of the flow. The second method is a full structure-from-motion solution. To make the comparison meaningful we use the same set of raw corresponding visual features for each method. The dataset is an sequence of 2000 images taken during a field experiment in the Atacama desert, for which high resolution GPS ground truth is available.

Communication and culture : mobile telephony in PNG villages

This paper examines the recent introduction of mobile telephony into rural communities in Papua New Guinea (PNG). It presents the findings of substantial fieldwork conducted in 2009, and suggests ways in which the new technology is already changing people’s lives and relationships. The paper identifies the roles of mobile telephones in two communities, the changes taking place and how villagers are responding to them. Comparison of the two villages is strategic as it highlights similarities in perceptions of mobile phones in these two very different settings. An ethnographic approach is adopted, situated within an interpretative methodology. Data collection methods include semi-structured interviews, orally-administered surveys and participant observation. The village lifestyle or ‘culture’ provides an important lens for understanding this data and the assertions made by village respondents. This research is significant as it addresses changes currently occurring in the communication methods of whole communities.

Robust outdoor visual localization using a three-dimensional-edge map

Visual localization systems that are practical for autonomous vehicles in outdoor industrial applications must perform reliably in a wide range of conditions. Changing outdoor conditions cause difficulty by drastically altering the information available in the camera images. To confront the problem, we have developed a visual localization system that uses a surveyed three-dimensional (3D)-edge map of permanent structures in the environment. The map has the invariant properties necessary to achieve long-term robust operation. Previous 3D-edge map localization systems usually maintain a single pose hypothesis, making it difficult to initialize without an accurate prior pose estimate and also making them susceptible to misalignment with unmapped edges detected in the camera image. A multihypothesis particle filter is employed here to perform the initialization procedure with significant uncertainty in the vehicle's initial pose. A novel observation function for the particle filter is developed and evaluated against two existing functions. The new function is shown to further improve the abilities of the particle filter to converge given a very coarse estimate of the vehicle's initial pose. An intelligent exposure control algorithm is also developed that improves the quality of the pertinent information in the image. Results gathered over an entire sunny day and also during rainy weather illustrate that the localization system can operate in a wide range of outdoor conditions. The conclusion is that an invariant map, a robust multihypothesis localization algorithm, and an intelligent exposure control algorithm all combine to enable reliable visual localization through challenging outdoor conditions.

Learning spatial concepts from RatSLAM representations

RatSLAM is a biologically-inspired visual SLAM and navigation system that has been shown to be effective indoors and outdoors on real robots. The spatial representation at the core of RatSLAM, the experience map, forms in a distributed fashion as the robot learns the environment. The activity in RatSLAM’s experience map possesses some geometric properties, but still does not represent the world in a human readable form. A new system, dubbed RatChat, has been introduced to enable meaningful communication with the robot. The intention is to use the “language games” paradigm to build spatial concepts that can be used as the basis for communication. This paper describes the first step in the language game experiments, showing the potential for meaningful categorization of the spatial representations in RatSLAM.

Are organ donation communication decisions reasoned or reactive? A test of the utility of an augmented Theory of Planned Behaviour with the Prototype/Willingness Model

Objectives: To explore whether people's organ donation consent decisions occur via a reasoned and/or social reaction pathway. --------- Design: We examined prospectively students' and community members' decisions to register consent on a donor register and discuss organ donation wishes with family. --------- Method: Participants completed items assessing theory of planned behaviour (TPB; attitude, subjective norm, perceived behavioural control (PBC)), prototype/willingness model (PWM; donor prototype favourability/similarity, past behaviour), and proposed additional influences (moral norm, self-identity, recipient prototypes) for registering (N=339) and discussing (N=315) intentions/willingness. Participants self-reported their registering (N=177) and discussing (N=166) behaviour 1 month later. The utility of the (1) TPB, (2) PWM, (3) augmented TPB with PWM, and (4) augmented TPB with PWM and extensions was tested using structural equation modelling for registering and discussing intentions/willingness, and logistic regression for behaviour. --------- Results: While the TPB proved a more parsimonious model, fit indices suggested that the other proposed models offered viable options, explaining greater variance in communication intentions/willingness. The TPB, augmented TPB with PWM, and extended augmented TPB with PWM best explained registering and discussing decisions. The proposed and revised PWM also proved an adequate fit for discussing decisions. Respondents with stronger intentions (and PBC for registering) had a higher likelihood of registering and discussing. --------- Conclusions: People's decisions to communicate donation wishes may be better explained via a reasoned pathway (especially for registering); however, discussing involves more reactive elements. The role of moral norm, self-identity, and prototypes as influences predicting communication decisions were highlighted also.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Effect of representation size and visual ambiguity on RatSLAM system performance

RatSLAM is a vision-based SLAM system based on extended models of the rodent hippocampus. RatSLAM creates environment representations that can be processed by the experience mapping algorithm to produce maps suitable for goal recall. The experience mapping algorithm also allows RatSLAM to map environments many times larger than could be achieved with a one to one correspondence between the map and environment, by reusing the RatSLAM maps to represent multiple sections of the environment. This paper describes experiments investigating the effects of the environment-representation size ratio and visual ambiguity on mapping and goal navigation performance. The experiments demonstrate that system performance is weakly dependent on either parameter in isolation, but strongly dependent on their joint values.

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.

Augmenting RatSLAM using FAB-MAP-based visual data association

This paper investigates the use of the FAB-MAP appearance-only SLAM algorithm as a method for performing visual data association for RatSLAM, a semi-metric full SLAM system. While both systems have shown the ability to map large (60-70km) outdoor locations of approximately the same scale, for either larger areas or across longer time periods both algorithms encounter difficulties with false positive matches. By combining these algorithms using a mapping between appearance and pose space, both false positives and false negatives generated by FAB-MAP are significantly reduced during outdoor mapping using a forward-facing camera. The hybrid FAB-MAP-RatSLAM system developed demonstrates the potential for successful SLAM over large periods of time.

Lip detection for audio-visual speech recognition in-car environment

Acoustically, car cabins are extremely noisy and as a consequence audio-only, in-car voice recognition systems perform poorly. As the visual modality is immune to acoustic noise, using the visual lip information from the driver is seen as a viable strategy in circumventing this problem by using audio visual automatic speech recognition (AVASR). However, implementing AVASR requires a system being able to accurately locate and track the drivers face and lip area in real-time. In this paper we present such an approach using the Viola-Jones algorithm. Using the AVICAR [1] in-car database, we show that the Viola- Jones approach is a suitable method of locating and tracking the driver’s lips despite the visual variability of illumination and head pose for audio-visual speech recognition system.

Visual localisation in outdoor industrial building environments

This paper presents a vision-based method of vehicle localisation that has been developed and tested on a large forklift type robotic vehicle which operates in a mainly outdoor industrial setting. The localiser uses a sparse 3D edgemap of the environment and a particle filter to estimate the pose of the vehicle. The vehicle operates in dynamic and non-uniform outdoor lighting conditions, an issue that is addressed by using knowledge of the scene to intelligently adjust the camera exposure and hence improve the quality of the information in the image. Results from the industrial vehicle are shown and compared to another laser-based localiser which acts as a ground truth. An improved likelihood metric, using peredge calculation, is presented and has shown to be 40% more accurate in estimating rotation. Visual localization results from the vehicle driving an arbitrary 1.5km path during a bright sunny period show an average position error of 0.44m and rotation error of 0.62deg.