Towards persistent visual navigation using SMART

In this paper, we present SMART (Sequence Matching Across Route Traversals): a vision- based place recognition system that uses whole image matching techniques and odometry information to improve the precision-recall performance, latency and general applicability of the SeqSLAM algorithm. We evaluate the system’s performance on challenging day and night journeys over several kilometres at widely varying vehicle velocities from 0 to 60 km/h, compare performance to the current state-of- the-art SeqSLAM algorithm, and provide parameter studies that evaluate the effectiveness of each system component. Using 30-metre sequences, SMART achieves place recognition performance of 81% recall at 100% precision, outperforming SeqSLAM, and is robust to significant degradations in odometry.

Co-production of actions and activities at airport security screening

In this paper we will examine passenger actions and activities at the security screening points of Australian domestic and international airports. Our findings and analysis provide a more complete understanding of the current airport passenger security screening experience. Data in this paper is comprised of field studies conducted at two Australian airports, one domestic and one international. Video data was collected by cameras situated either side of the security screening point. A total of one hundred and ninety-six passengers were observed. Two methods of analysis are used. First, the activities of passengers are coded and analysed to reveal the common activities at domestic and international security regimes and between quiet and busy periods. Second, observation of passenger activities is used to reveal uncommon aspects. The results show that passengers do more at security screening that being passively scanned. Passengers queue, unpack the required items from their bags and from their pockets, walk through the metal-detector, re-pack and occasionally return to be re-screened. For each of these activities, passengers must understand the procedures at the security screening point and must co-ordinate various actions and objects in time and space. Through this coordination passengers are active participants in making the security checkpoint function – they are co-producers of the security screening process.

Structural dynamics education in the new millennium

Structural Dynamics is the study of the response of structures to dynamic or time varying loads. This topic has emerged to be one of importance to all structural engineers due to three important issues with structural engineering in the new millennium. These are: (1) vibration and problems in slender structures that have emerged due to new material technology and aesthetic requirements, (ii) ageing structures such as bridges whoese health needs to be monitored and appropriate retrofitting carried out to prevent failure and (iii) increased vulnerability of structures to random loads such as seismic, impact and blast loads. Knowledge of structural dynamics is necessary to address these issues and their consequences. During the past two decades, research in structural dynamics has generated considerable amount of new information to address these issues. This new knowledge is not readily made available to practicing engineers and very little or none of it enters the classrooms. There is no universal emphasis on including structural dynamics and their recently generated new knowledge into the civil/structural curriculum. This paper argues for the need to include structural dynamics into the syllabus of all civil engineering courses especially those having a first or second major in structural engineering. This will enable our future structural engineers to design and maintain safe and efficient structures.

FRC erection stages dynamic behaviour under wind loads

Buffeting response of a cable-stayed bridge under construction is investigated through wind tunnel tests and numerical simulations. Two configurations of the erection stage have been considered and compared in terms of dynamic response and internal forces using the results of the experimental aeroelastic models. Moreover the results of a numerical model able to simulate the simultaneous effects of vortex shedding from tower and aeroelastic response of the deck are compared to the wind tunnel ones.

A novel method for analysing lighting variance

Robust descriptor matching across varying lighting conditions is important for vision-based robotics. We present a novel strategy for quantifying the lighting variance of descriptors. The strategy works by utilising recovered low dimensional mappings from Isomap and our measure of the lighting variance of each of these mappings. The resultant metric allows different descriptors to be compared given a dataset and a set of keypoints. We demonstrate that the SIFT descriptor typically has lower lighting variance than other descriptors, although the result depends on semantic class and lighting conditions.

Advantages of exploiting projection structure for segmenting dense 3D point clouds

Timely and comprehensive scene segmentation is often a critical step for many high level mobile robotic tasks. This paper examines a projected area based neighbourhood lookup approach with the motivation towards faster unsupervised segmentation of dense 3D point clouds. The proposed algorithm exploits the projection geometry of a depth camera to find nearest neighbours which is time independent of the input data size. Points near depth discontinuations are also detected to reinforce object boundaries in the clustering process. The search method presented is evaluated using both indoor and outdoor dense depth images and demonstrates significant improvements in speed and precision compared to the commonly used Fast library for approximate nearest neighbour (FLANN) [Muja and Lowe, 2009].

Low cost localisation for agricultural robotics

This paper presents a pose estimation approach that is resilient to typical sensor failure and suitable for low cost agricultural robots. Guiding large agricultural machinery with highly accurate GPS/INS systems has become standard practice, however these systems are inappropriate for smaller, lower-cost robots. Our positioning system estimates pose by fusing data from a low-cost global positioning sensor, low-cost inertial sensors and a new technique for vision-based row tracking. The results first demonstrate that our positioning system will accurately guide a robot to perform a coverage task across a 6 hectare field. The results then demonstrate that our vision-based row tracking algorithm improves the performance of the positioning system despite long periods of precision correction signal dropout and intermittent dropouts of the entire GPS sensor.

μAV - Design and implementation of an open source micro quadrotor

This paper presents the design of μAV, a palm size open source micro quadrotor constructed on a single Printed Circuit Board. The aim of the micro quadrotor is to provide a lightweight (approximately 86g) and cheap robotic research platform that can be used for a range of robotic applications. One possible application could be a cheap test bed for robotic swarm research. The goal of this paper is to give an overview of the design and capabilities of the micro quadrotor. The micro quadrotor is complete with a 9 Degree of Freedom Inertial Measurement Unit, a Gumstix Overo® Computer-On-Module which can run the widely used Robot Operating System (ROS) for use with other research algorithms.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Improved line tracker using IMU and vision for visual servoing

This paper introduces an improved line tracker using IMU and vision data for visual servoing tasks. We utilize an Image Jacobian which describes motion of a line feature to corresponding camera movements. These camera motions are estimated using an IMU. We demonstrate impacts of the proposed method in challenging environments: maximum angular rate ~160 0/s, acceleration ~6m /s2 and in cluttered outdoor scenes. Simulation and quantitative tracking performance comparison with the Visual Servoing Platform (ViSP) are also presented.

Towards bio-inspired place recognition over multiple spatial scales

This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to recognize places at varying levels of spatial specificity, we are able to validate spatially specific place recognition hypotheses against broader place recognition hypotheses without sacrificing localization accuracy. We evaluate the system in a range of experiments using cameras mounted on a motorbike and a human in two different environments. At 100% precision, the multiscale approach results in a 56% average improvement in recall rate across both datasets. We analyse the results and then discuss future work that may lead to improvements in both robotic mapping and our understanding of sensory processing and encoding in the mammalian brain.

Towards condition-invariant, top-down visual place recognition

In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.

An analysis of regression models for predicting the speed of a wave glider autonomous surface vehicle

An important aspect of robotic path planning for is ensuring that the vehicle is in the best location to collect the data necessary for the problem at hand. Given that features of interest are dynamic and move with oceanic currents, vehicle speed is an important factor in any planning exercises to ensure vehicles are at the right place at the right time. Here, we examine different Gaussian process models to find a suitable predictive kinematic model that enable the speed of an underactuated, autonomous surface vehicle to be accurately predicted given a set of input environmental parameters.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Dispute resolution across platforms : offshore gambling industry & EVE online

Contemporary online environments suffer from a regulatory gap; that is there are few options for participants between customer service departments and potentially expensive court cases in foreign jurisdictions. Whatever form of regulation ultimately fills that gap will be charged with determining whether specific behavior, within a specific environment, is fair or foul; whether it’s cheating or not. However, cheating is a term that, despite substantial academic study, remains problematic. Is anything the developer doesn’t want you to do cheating? Is it only if your actions breach the formal terms of service? What about the community norms, do they matter at all? All of these remain largely unresolved questions, due to the lack of public determination of cases in such environments, which have mostly been settled prior to legal action. In this paper, I propose a re-branding of participant activity in such environments into developer-sanctioned, advantage play, and cheating. Advantage play, ultimately, is activity within the environment in which the player is able to turn the mechanics of the environment to their advantage without breaching the rules of the environment. Such a definition, and the term itself, is based on the usage of the term within the gambling industry, in which advantage play is considered betting with the advantage in the players’ favor rather than that of the house. Through examples from both the gambling industry and the Massively Multiplayer Role-Playing Game Eve Online, I consider the problems in defining cheating, suggest how the term ‘advantage play’ may be useful in understanding participants behavior in contemporary environments, and ultimately consider the use of such terminology in dispute resolution models which may overcome this regulatory gap.