Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment

Many localization algorithms use a spatiotemporal window of sensory information in order to recognize spatial locations, and the length of this window is often a sensitive parameter that must be tuned to the specifics of the application. This letter presents a general method for environment-driven variation of the length of the spatiotemporal window based on searching for the most significant localization hypothesis, to use as much context as is appropriate but not more. We evaluate this approach on benchmark datasets using visual and Wi-Fi sensor modalities and a variety of sensory comparison front-ends under in-order and out-of-order traversals of the environment. Our results show that the system greatly reduces the maximum distance traveled without localization compared to a fixed-length approach while achieving competitive localization accuracy, and our proposed method achieves this performance without deployment-time tuning.Comment: Pre-print of article appearing in 2017 IEEE Robotics and Automation Letters. v2: incorporated reviewer feedbac

Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration

Convolutional Neural Networks (CNNs) have recently been shown to excel at performing visual place recognition under changing appearance and viewpoint. Previously, place recognition has been improved by intelligently selecting relevant spatial keypoints within a convolutional layer and also by selecting the optimal layer to use. Rather than extracting features out of a particular layer, or a particular set of spatial keypoints within a layer, we propose the extraction of features using a subset of the channel dimensionality within a layer. Each feature map learns to encode a different set of weights that activate for different visual features within the set of training images. We propose a method of calibrating a CNN-based visual place recognition system, which selects the subset of feature maps that best encodes the visual features that are consistent between two different appearances of the same location. Using just 50 calibration images, all collected at the beginning of the current environment, we demonstrate a significant and consistent recognition improvement across multiple layers for two different neural networks. We evaluate our proposal on three datasets with different types of appearance changes - afternoon to morning, winter to summer and night to day. Additionally, the dimensionality reduction approach improves the computational processing speed of the recognition system.Comment: Accepted to the Australasian Conference on Robotics and Automation 201

Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and Automation Letter

Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory

The Pepper robot has become a widely recognised face for the perceived potential of social robots to enter our homes and businesses. However, to date, commercial and research applications of the Pepper have been largely restricted to roles in which the robot is able to remain stationary. This restriction is the result of a number of technical limitations, including limited sensing capabilities, and have as a result, reduced the number of roles in which use of the robot can be explored. In this paper, we present our approach to solving these problems, with the intention of opening up new research applications for the robot. To demonstrate the applicability of our approach, we have framed this work within the context of providing interactive tours of an open-plan robotics laboratory.Comment: 8 pages, Submitted to IROS 2018 (2018 IEEE/RSJ International Conference on Intelligent Robots and Systems), see https://bitbucket.org/pepper_qut/ for access to the softwar

Vacuum Cherenkov radiation and photon triple-splitting in a Lorentz-noninvariant extension of quantum electrodynamics

We consider a CPT-noninvariant scalar model and a modified version of quantum electrodynamics with an additional photonic Chern-Simons-like term in the action. In both cases, the Lorentz violation traces back to a spacelike background vector. The effects of the modified field equations and dispersion relations on the kinematics and dynamics of decay processes are discussed, first for the simple scalar model and then for modified quantum electrodynamics. The decay widths for electron Cherenkov radiation in modified quantum electrodynamics and for photon triple-splitting in the corresponding low-energy effective theory are obtained to lowest order in the electromagnetic coupling constant. A conjecture for the high-energy limit of the photon-triple-splitting decay width at tree level is also presented.Comment: elsart, 30 pages, v4: published versio

Deep Learning Features at Scale for Visual Place Recognition

The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area.Comment: 8 pages, 10 figures. Accepted by International Conference on Robotics and Automation (ICRA) 2017. This is the submitted version. The final published version may be slightly differen

High speed driving stability of road vehicles under crosswinds: an aerodynamic and vehicle dynamic parametric sensitivity analysis

Crosswinds affect vehicle driving stability and their influence increase with driving speed. To improve high speed driving stability, interdisciplinary research using unsteady aerodynamics and vehicle dynamics is necessary. The current demands of faster development times require robust virtual methods for assessing stability performance in early design phases. This paper employs a numerical one-way coupling between the two disciplines and uses a variety of realistic crosswind gust profiles for the aerodynamic simulations to output representative forces and moments on three vehicle dynamic models of different fidelity levels, ranging from a one-track model to a full multi-body dynamic model of a sports utility vehicle. An investigation on required model fidelity was conducted along with a sensitivity study to find key aerodynamic and vehicle dynamic characteristics to minimise the yaw velocity and lateral acceleration response during crosswinds. Transient aerodynamic simulations were used to model crosswind gusts at high speeds. Analysis of the forces and moments showed that rapid changing gusts generate overshoots in the yaw moment, due to the phase delay of the flow between the front and rear of the vehicle. A methodology for modelling this phase delay is proposed. The response of the vehicle was captured equally well by the enhanced model (mid-level fidelity) and the full multi-body dynamic model, while the simplest one-track model failed to emulate the correct vehicle response. The sensitivity study showed the importance of the positioning of the centre of gravity, the aerodynamic coefficient of yaw moment, wheel base, vehicle mass and yaw inertia. In addition, the axles\u27 side force steer gradients and other suspension parameters revealed potential in improving crosswind stability

Base wake dynamics and its influence on driving stability of passenger vehicles in crosswind

The unsteady flow around a travelling vehicle induces fluctuating aerodynamic loads. Automotive manufacturers usually set targets on the time-averaged lift forces to ensure good straight-line stability performance at high speeds. These targets are generally sufficient in preventing unstable vehicle designs. Yet, small changes in averaged values occasionally yield unexpectedly large differences in the stability performance, indicating that the changes in averaged normal loads cannot solely explain these differences. The unsteady aerodynamic effects on driving stability are, therefore, an interesting topic to study. The objective of the present work is to investigate the differences in wake dynamics and fluctuating aerodynamic loads for two variants of a roof spoiler on a sports utility vehicle: a baseline that was known to cause stability issues and an improved design which resolved them. The vehicle designs were investigated using accurate time-resolved CFD simulations for a set of crosswind conditions. The unsteady aerodynamic response was coupled to a vehicle dynamics model to analyse the resulting impact on driving stability. It was shown that in crosswinds the baseline spoiler, contrary to the improved spoiler, has bi-stable wake dynamics that induce lift force fluctuations at frequencies close to the 1st natural frequency of the rear suspension

Convolutional Neural Network-based Place Recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.Comment: 8 pages, 11 figures, this paper has been accepted by 2014 Australasian Conference on Robotics and Automation (ACRA 2014) to be held in University of Melbourne, Dec 2~