Centrifuging rotary vacuum filter mud at Tully Mill

Disposal of mud and ash, particularly in wet weather conditions, is a significant expense for mills. This paper reports on one part of a process to pelletise mud and ash, aimed at making mud and ash more attractive to growers across entire mill districts. The full process is described in a separate paper. The part described in this paper involves re-constituting mud cake from the filter station at Tully Mill and processing it in a decanter centrifuge. The material produced by re-constituting and centrifuging is drier and made up of separate particles. The material needs to mix easily with boiler ash, and the mixture needs to be fed easily into a flue gas drier to be dried to low moisture. The results achieved with the particular characteristics of Tully Mill rotary vacuum filter cake are presented. It was found that an internal rotor with a 20º beach was not adequate to process re-constituted rotary vacuum filter mud. A rotor with a 10º beach worked much more successfully. A total of four tonnes of centrifuged mud with a moisture content ranging from 60% to 65% was produced. It was found that the torque, flocculant rate and dose rate had a statistically significant effect on the moisture content. Feed rate did not have a noticeable impact on the moisture content by itself but torque had a much larger impact on the moisture content at the low feed rate than at the high feed rate. These results indicated that the moisture content of the mud can most likely be reduced with low feed rate, low flocculant rate, high dose rate and high torque. One issue that is believed to affect the operation of a decanter centrifuge was the large quantity of long bagasse fibres in the rotary vacuum filter mud. It is likely that the long fibres limited the throughput of the centrifuge and the moisture achieved.

Robust scale initialization for long-range stereo visual odometry

Achieving a robust, accurately scaled pose estimate in long-range stereo presents significant challenges. For large scene depths, triangulation from a single stereo pair is inadequate and noisy. Additionally, vibration and flexible rigs in airborne applications mean accurate calibrations are often compromised. This paper presents a technique for accurately initializing a long-range stereo VO algorithm at large scene depth, with accurate scale, without explicitly computing structure from rigidly fixed camera pairs. By performing a monocular pose estimate over a window of frames from a single camera, followed by adding the secondary camera frames in a modified bundle adjustment, an accurate, metrically scaled pose estimate can be found. To achieve this the scale of the stereo pair is included in the optimization as an additional parameter. Results are presented both on simulated and field gathered data from a fixed-wing UAV flying at significant altitude, where the epipolar geometry is inaccurate due to structural deformation and triangulation from a single pair is insufficient. Comparisons are made with more conventional VO techniques where the scale is not explicitly optimized, and demonstrated over repeated trials to indicate robustness.

Long-range stereo visual odometry for unmanned aerial vehicles

This thesis explored the utility of long-range stereo visual odometry for application on Unmanned Aerial Vehicles. Novel parameterisations and initialisation routines were developed for the long-range case of stereo visual odometry and new optimisation techniques were implemented to improve the robustness of visual odometry in this difficult scenario. In doing so, the applications of stereo visual odometry were expanded and shown to perform adequately in situations that were previously unworkable.

Visual performance at passive level crossings with long sighting distances

The third edition of the Australian Standard AS1742 Manual of Uniform Traffic Control Devices Part 7 provides a method of calculating the sighting distance required to safely proceed at passive level crossings based on the physics of moving vehicles. This required distance becomes greater with higher line speeds and slower, heavier vehicles so that it may return quite a long sighting distance. However, at such distances, there are also concerns around whether drivers would be able to reliably identify a train in order to make an informed decision regarding whether it would be safe to proceed across the level crossing. In order to determine whether drivers are able to make reliable judgements to proceed in these circumstances, this study assessed the distance at which a train first becomes identifiable to a driver as well as their, ability to detect the movement of the train. A site was selected in Victoria, and 36 participants with good visual acuity observed 4 trains in the 100-140 km/h range. While most participants could detect the train from a very long distance (2.2 km on average), they could only detect that the train was moving at much shorter distances (1.3 km on average). Large variability was observed between participants, with 4 participants consistently detecting trains later than other participants. Participants tended to improve in their capacity to detect the presence of the train with practice, but a similar trend was not observed for detection of the movement of the train. Participants were consistently poor at accurately judging the approach speed of trains, with large underestimations at all investigated distances.

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.

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.

Visual backward masking performance in young adult emmetropes and myopes

Purpose. To investigate how temporal processing is altered in myopia and during myopic progression. Methods. In backward visual masking, a target's visibility is reduced by a mask presented quickly after the target. Thirty emmetropes, 40 low myopes, and 22 high myopes aged 18 to 26 years completed location and resolution masking tasks. The location task examined the ability to detect letters with low contrast and large stimulus size. The resolution task involved identifying a small letter and tested resolution and color discrimination. Target and mask stimuli were presented at nine short interstimulus intervals (12 to 259 ms) and at 1000 ms (long interstimulus interval condition). Results. In comparison with emmetropes, myopes had reduced ability in both locating and identifying briefly presented stimuli but were more affected by backward masking for a low contrast location task than for a resolution task. Performances of low and high myopes, as well as stable and progressing myopes, were similar for both masking tasks. Task performance was not correlated with myopia magnitude. Conclusions. Myopes were more affected than emmetropes by masking stimuli for the location task. This was not affected by magnitude or progression rate of myopia, suggesting that myopes have the propensity for poor performance in locating briefly presented low contrast objects at an early stage of myopia development.

Why the long face?

In this video, a male voice recites a script comprised entirely of jokes. Words flash on screen in time with the spoken words. Sometimes the two sets of words match, and sometimes they differ. This work examines processes of signification. It emphasizes disruption and disconnection as fundamental and generative operations in making meaning. Extending on post-structural and deconstructionist ideas, this work questions the relationship between written and spoken words. By deliberately confusing the signifying structures of jokes and narratives, it questions the sites and mechanisms of comprehension, humour and signification.

Visual acuity is not what it seems : on Ian Burn's 'Late' reflections

A whole tradition is said to be based on the hierarchical distinction between the perceptual and conceptual. In art, Niklas Luhmann argues, this schism is played out and repeated in conceptual art. This paper complicates this depiction by examining Ian Burn's last writings in which I argue the artist-writer reviews the challenge of minimal-conceptual art in terms of its perceptual pre-occupations. Burn revisits his own work and the legacy of minimal-conceptual by moving away from the kind of ideology critique he is best known for internationally in order to reassert the long overlooked visual-perceptual preoccupations of the conceptual in art.

Bi-objective bundle adjustment: towards large-scale visual sea-floor mapping with a minimal sensor suite

Visual sea-floor mapping is a rapidly growing application for Autonomous Underwater Vehicles (AUVs). AUVs are well-suited to the task as they remove humans from a potentially dangerous environment, can reach depths human divers cannot, and are capable of long-term operation in adverse conditions. The output of sea-floor maps generated by AUVs has a number of applications in scientific monitoring: from classifying coral in high biological value sites to surveying sea sponges to evaluate marine environment health.

Long exposure localization in darkness using consumer cameras

In this paper we demonstrate passive vision-based localization in environments more than two orders of magnitude darker than the current benchmark using a 100 webcam and a 500 camera. Our approach uses the camera’s maximum exposure duration and sensor gain to achieve appropriately exposed images even in unlit night-time environments, albeit with extreme levels of motion blur. Using the SeqSLAM algorithm, we first evaluate the effect of variable motion blur caused by simulated exposures of 132 ms to 10000 ms duration on localization performance. We then use actual long exposure camera datasets to demonstrate day-night localization in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed greyscale images to using patch normalization and local neighbourhood normalization – the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function across extreme perceptual change.

Online calibration of stereo rigs for long-term autonomy

Stereo-based visual odometry algorithms are heavily dependent on an accurate calibration of the rigidly fixed stereo pair. Even small shifts in the rigid transform between the cameras can impact on feature matching and 3D scene triangulation, adversely affecting pose estimates and applications dependent on long-term autonomy. In many field-based scenarios where vibration, knocks and pressure change affect a robotic vehicle, maintaining an accurate stereo calibration cannot be guaranteed over long periods. This paper presents a novel method of recalibrating overlapping stereo camera rigs from online visual data while simultaneously providing an up-to-date and up-to-scale pose estimate. The proposed technique implements a novel form of partitioned bundle adjustment that explicitly includes the homogeneous transform between a stereo camera pair to generate an optimal calibration. Pose estimates are computed in parallel to the calibration, providing online recalibration which seamlessly integrates into a stereo visual odometry framework. We present results demonstrating accurate performance of the algorithm on both simulated scenarios and real data gathered from a wide-baseline stereo pair on a ground vehicle traversing urban roads.

Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views

This paper presents a long-term experiment where a mobile robot uses adaptive spherical views to localize itself and navigate inside a non-stationary office environment. The office contains seven members of staff and experiences a continuous change in its appearance over time due to their daily activities. The experiment runs as an episodic navigation task in the office over a period of eight weeks. The spherical views are stored in the nodes of a pose graph and they are updated in response to the changes in the environment. The updating mechanism is inspired by the concepts of long- and short-term memories. The experimental evaluation is done using three performance metrics which evaluate the quality of both the adaptive spherical views and the navigation over time.

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.

Selective combination of visual and thermal imaging for resilient localization in adverse conditions : day and night, smoke and fire

Long-term autonomy in robotics requires perception systems that are resilient to unusual but realistic conditions that will eventually occur during extended missions. For example, unmanned ground vehicles (UGVs) need to be capable of operating safely in adverse and low-visibility conditions, such as at night or in the presence of smoke. The key to a resilient UGV perception system lies in the use of multiple sensor modalities, e.g., operating at different frequencies of the electromagnetic spectrum, to compensate for the limitations of a single sensor type. In this paper, visual and infrared imaging are combined in a Visual-SLAM algorithm to achieve localization. We propose to evaluate the quality of data provided by each sensor modality prior to data combination. This evaluation is used to discard low-quality data, i.e., data most likely to induce large localization errors. In this way, perceptual failures are anticipated and mitigated. An extensive experimental evaluation is conducted on data sets collected with a UGV in a range of environments and adverse conditions, including the presence of smoke (obstructing the visual camera), fire, extreme heat (saturating the infrared camera), low-light conditions (dusk), and at night with sudden variations of artificial light. A total of 240 trajectory estimates are obtained using five different variations of data sources and data combination strategies in the localization method. In particular, the proposed approach for selective data combination is compared to methods using a single sensor type or combining both modalities without preselection. We show that the proposed framework allows for camera-based localization resilient to a large range of low-visibility conditions.