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  • 1.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Improving Localisation Accuracy using Submaps in warehouses2018Conference paper (Other academic)
    Abstract [en]

    This paper presents a method for localisation in hybrid metric-topological maps built using only local information that is, only measurements that were captured by the robot when it was in a nearby location. The motivation is that observations are typically range and viewpoint dependent and that a map a discrete map representation might not be able to explain the full structure within a voxel. The localisation system uses a method to select submap based on how frequently and where from each submap was updated. This allow the system to select the most descriptive submap, thereby improving the localisation and increasing performance by up to 40%.

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    Improving Localisation Accuracy using Submaps in warehouses
  • 2.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    A Submap per Perspective: Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality2019In: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.

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    A Submap per Perspective - Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
  • 3.
    Andreasson, Henrik
    et al.
    Örebro University, School of Science and Technology.
    Larsson, Jonas
    ABB Corporate Research, Västerås, Sweden.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 7, article id 2588Article in journal (Refereed)
    Abstract [en]

    This paper presents a local planning approach that is targeted for pseudo-omnidirectional vehicles: that is, vehicles that can drive sideways and rotate on the spot. This local planner—MSDU–is based on optimal control and formulates a non-linear optimization problem formulation that exploits the omni-motion capabilities of the vehicle to drive the vehicle to the goal in a smooth and efficient manner while avoiding obstacles and singularities. MSDU is designed for a real platform for mobile manipulation where one key function is the capability to drive in narrow and confined areas. The real-world evaluations show that MSDU planned paths that were smoother and more accurate than a comparable local path planner Timed Elastic Band (TEB), with a mean (translational, angular) error for MSDU of (0.0028 m, 0.0010 rad) compared to (0.0033 m, 0.0038 rad) for TEB. MSDU also generated paths that were consistently shorter than TEB, with a mean (translational, angular) distance traveled of (0.6026 m, 1.6130 rad) for MSDU compared to (0.7346 m, 3.7598 rad) for TEB.

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    A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization
  • 4.
    Chen, Zetao
    et al.
    The ARC Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia .
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Jacobson, Adam
    The ARC Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia .
    Ge, ZongYuan
    The ARC Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia .
    Milford, Michael
    The ARC Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia .
    Distance metric learning for feature-agnostic place recognition2015In: Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2015, p. 2556-2563Conference paper (Refereed)
    Abstract [en]

    The recent focus on performing visual navigation and place recognition in changing environments has resulted in a large number of heterogeneous techniques each utilizing their own learnt or hand crafted visual features. This paper presents a generally applicable method for learning the appropriate distance metric by which to compare feature responses from any of these techniques in order to perform place recognition under changing environmental conditions. We implement an approach which learns to cluster images captured at spatially proximal locations under different conditions, separated from frames captured at different places. The formulation is a convex optimization, guaranteeing the existence of a global solution. We evaluate the general applicability of our method on two benchmark change datasets using three typical image pre-processing and feature types: GIST, Principal Component Analysis and learnt Convolutional Neural Network features. The results demonstrate that the distance metric learning approach uniformly improves single-image-based visual place recognition performance across all feature types. Furthermore, we demonstrate that this performance improvement is maintained when the sequence-based algorithm SeqSLAM is applied to the single-image place recognition results, leading to state-of-the-art performance.

  • 5.
    Chen, Zetao
    et al.
    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane Qld, Australia; Australian Centre for Robotic Vision, Queensland University of Technology, Brisbane Qld, Australia.
    Lowry, Stephanie
    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane Qld, Australia.
    Jacobson, Adam
    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane Qld, Australia; Australian Centre for Robotic Vision, Queensland University of Technology, Brisbane Qld, Australia.
    Hasselmo, Michael E.
    Center for Memory and Brain and Graduate Program for Neuroscience, Boston University, Boston, United States.
    Milford, Michael
    School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane Qld, Australia; Australian Centre for Robotic Vision, Queensland University of Technology, Brisbane Qld, Australia.
    Bio-inspired homogeneous multi-scale place recognition2015In: Neural Networks, ISSN 0893-6080, E-ISSN 1879-2782, Vol. 72, p. 48-61Article in journal (Refereed)
    Abstract [en]

    Robotic mapping and localization systems typically operate at either one fixed spatial scale, or over two, combining a local metric map and a global topological map. In contrast, recent high profile discoveries in neuroscience have indicated that animals such as rodents navigate the world using multiple parallel maps, with each map encoding the world at a specific spatial scale. While a number of theoretical-only investigations have hypothesized several possible benefits of such a multi-scale mapping system, no one has comprehensively investigated the potential mapping and place recognition performance benefits for navigating robots in large real world environments, especially using more than two homogeneous map scales. In this paper we present a biologically-inspired multi-scale mapping system mimicking the rodent multi-scale map. Unlike hybrid metric-topological multi-scale robot mapping systems, this new system is homogeneous, distinguishable only by scale, like rodent neural maps. We present methods for training each network to learn and recognize places at a specific spatial scale, and techniques for combining the output from each of these parallel networks. This approach differs from traditional probabilistic robotic methods, where place recognition spatial specificity is passively driven by models of sensor uncertainty. Instead we intentionally create parallel learning systems that learn associations between sensory input and the environment at different spatial scales. We also conduct a systematic series of experiments and parameter studies that determine the effect on performance of using different neural map scaling ratios and different numbers of discrete map scales. The results demonstrate that a multi-scale approach universally improves place recognition performance and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms. We analyze the results and discuss the implications with respect to several recent discoveries and theories regarding how multi-scale neural maps are learnt and used in the mammalian brain.

  • 6.
    Kucner, Tomasz Piotr
    et al.
    Örebro University, School of Science and Technology.
    Luperto, Matteo
    Applied Intelligent System Lab (AISLab), Università degli Studi di Milano, Milano, Italy.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Robust Frequency-Based Structure Extraction2021In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021, p. 1715-1721Conference paper (Refereed)
    Abstract [en]

    State of the art mapping algorithms can produce high-quality maps. However, they are still vulnerable to clutter and outliers which can affect map quality and in consequence hinder the performance of a robot, and further map processing for semantic understanding of the environment. This paper presents ROSE, a method for building-level structure detection in robotic maps. ROSE exploits the fact that indoor environments usually contain walls and straight-line elements along a limited set of orientations. Therefore metric maps often have a set of dominant directions. ROSE extracts these directions and uses this information to segment the map into structure and clutter through filtering the map in the frequency domain (an approach substantially underutilised in the mapping applications). Removing the clutter in this way makes wall detection (e.g. using the Hough transform) more robust. Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e.g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.

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    Robust Frequency-Based Structure Extraction
  • 7.
    Kurtser, Polina
    et al.
    Örebro University, School of Science and Technology. Department of Radiation Science, Radiation Physics, Umeå University, Sweden.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    RGB-D datasets for robotic perception in site-specific agricultural operations: A survey2023In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 212, article id 108035Article, review/survey (Refereed)
    Abstract [en]

    Fusing color (RGB) images and range or depth (D) data in the form of RGB-D or multi-sensory setups is a relatively new but rapidly growing modality for many agricultural tasks. RGB-D data have potential to provide valuable information for many agricultural tasks that rely on perception, but collection of appropriate data and suitable ground truth information can be challenging and labor-intensive, and high-quality publicly available datasets are rare. This paper presents a survey of the existing RGB-D datasets available for agricultural robotics, and summarizes key trends and challenges in this research field. It evaluates the relative advantages of the commonly used sensors, and how the hardware can affect the characteristics of the data collected. It also analyzes the role of RGB-D data in the most common vision-based machine learning tasks applied to agricultural robotic operations: visual recognition, object detection, and semantic segmentation, and compares and contrasts methods that utilize 2-D and 3-D perceptual data.

  • 8.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Similarity criteria: evaluating perceptual change for visual localization2019In: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019, article id 8870962Conference paper (Refereed)
    Abstract [en]

    Visual localization systems may operate in environments that exhibit considerable perceptual change. This paper proposes a method of evaluating the degree of appearance change using a similarity criteria based on comparing the subspaces spanned by the principal components of the observed image descriptors. We propose two criteria - θmin measures the minimum angle between subspaces and Stotal measures the total similarity between the subspaces. These criteria are introspective - they evaluate the performance of the image descriptor using nothing more than the image descriptor itself. Furthermore, we demonstrate that these similarity criteria reflect the ability of the image descriptor to perform visual localization successfully, thus allowing a measure of quality control on the localization output.

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    Similarity criteria: Evaluating perceptual change for visual localization
  • 9.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lightweight, Viewpoint-Invariant Visual Place Recognition in Changing Environments2018In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 3, no 2, p. 957-964Article in journal (Refereed)
    Abstract [en]

    This paper presents a viewpoint-invariant place recognition algorithm which is robust to changing environments while requiring only a small memory footprint. It demonstrates that condition-invariant local features can be combined with Vectors of Locally Aggregated Descriptors (VLAD) to reduce high-dimensional representations of images to compact binary signatures while retaining place matching capability across visually dissimilar conditions. This system provides a speed-up of two orders of magnitude over direct feature matching, and outperforms a bag-of-visual-words approach with near-identical computation speed and memory footprint. The experimental results show that single-image place matching from non-aligned images can be achieved in visually changing environments with as few as 256 bits (32 bytes) per image.

  • 10.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    LOGOS: Local geometric support for high-outlier spatial verification2018Conference paper (Refereed)
    Abstract [en]

    This paper presents LOGOS, a method of spatial verification for visual localization that is robust in the presence of a high proportion of outliers. LOGOS uses scale and orientation information from local neighbourhoods of features to determine which points are likely to be inliers. The inlier points can be used for secondary localization verification and pose estimation. LOGOS is demonstrated on a number of benchmark localization datasets and outperforms RANSAC as a method of outlier removal and localization verification in scenarios that require robustness to many outliers.

  • 11.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Visual place recognition techniques for pose estimation in changing environments2016In: Visual Place Recognition: What is it Good For? workshop, Robotics: Science and Systems (RSS) 2016, 2016Conference paper (Other academic)
    Abstract [en]

    This paper investigates whether visual place recognition techniques can be used to provide pose estimation information for a visual SLAM system operating long-term in an environment where the appearance may change a great deal. It demonstrates that a combination of a conventional SURF feature detector and a condition-invariant feature descriptor such as HOG or conv3 can provide a method of determining the relative transformation between two images, even when there is both appearance change and rotation or viewpoint change.

    Download full text (pdf)
    Visual place recognition techniques for pose estimation in changing environments
  • 12.
    Lowry, Stephanie
    et al.
    The ARC Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia .
    Milford, Michael
    The ARC Australian Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia.
    Building Beliefs: Unsupervised Generation of Observation Likelihoods for Probabilistic Localization in Changing Environments2015In: IEEE International Conference on Intelligent Robots and Systems (IROS), IEEE, 2015, New York, USA: IEEE, 2015, p. 3071-3078Conference paper (Refereed)
    Abstract [en]

    This paper is concerned with the interpretation of visual information for robot localization. It presents a probabilistic localization system that generates an appropriate observation model online, unlike existing systems which require pre-determined belief models. This paper proposes that probabilistic visual localization requires two major operating modes - one to match locations under similar conditions and the other to match locations under different conditions. We develop dual observation likelihood models to suit these two different states, along with a similarity measure-based method that identifies the current conditions and switches between the models. The system is experimentally tested against different types of ongoing appearance change. The results demonstrate that the system is compatible with a wide range of visual front-ends, and the dual-model system outperforms a single-model or pre-trained approach and state-of-the-art localization techniques.

  • 13.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Milford, Michael
    Queensland University of Technology, Brisbane, Australia.
    Supervised and Unsupervised Linear Learning Techniques for Visual Place Recognition in Changing Environments2016In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 3, p. 600-613Article in journal (Refereed)
    Abstract [en]

    This paper investigates the application of linear learning techniques to the place recognition problem. We present two learning methods, a supervised change prediction technique based on linear regression and an unsupervised change removal technique based on principal component analysis, and investigate how the performance of each is affected by the choice of training data. We show that the change prediction technique presented here succeeds only if it is provided with appropriate and adequate training data, which can be challenging for a mobile robotic system operating in an uncontrolled environment. In contrast, change removal can improve place recognition performance even when trained with as few as 100 samples. This paper shows that change removal can be combined with a number of different image descriptors and can improve performance across a range of different appearance conditions.

  • 14.
    Lowry, Stephanie
    et al.
    Örebro University, School of Science and Technology.
    Sunderhauf, Niko
    The Australian Centre for Robotic Vision, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia.
    Newman, Paul
    The Mobile Robotics Group, Department of Engineering Science, University of Oxford, Oxford, U.K..
    Leonard, John
    The Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA.
    Cox, David
    The Department of Molecular and Cellular Biology, the School of Engineering and Applied Science, and the Center for Brain Science, Harvard University, Cambridge, USA.
    Corke, Peter
    The Australian Centre for Robotic Vision, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia.
    Milford, Michael
    The Australian Centre for Robotic Vision, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia.
    Visual Place Recognition: A Survey2016In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 32, no 1, p. 1-19Article in journal (Refereed)
    Abstract [en]

    Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines - particularly recognition in computer vision and animal navigation in neuroscience - have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition - the role of place recognition in the animal kingdom, how a "place" is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.

  • 15.
    Milford, Michael
    et al.
    Australian Centre for Robotic Vision, Queensland University of Technology Australia, Brisbane, Australia .
    Shen, Chunhua
    Australian Centre for Robotic Vision, The University of Adelaide, Adelaide, Australia.
    Lowry, Stephanie
    Australian Centre for Robotic Vision, Queensland University of Technology Australia, Brisbane, Australia .
    Sünderhauf, Niko
    Australian Centre for Robotic Vision, Queensland University of Technology Australia, Brisbane, Australia .
    Shirazi, Sareh
    Australian Centre for Robotic Vision, Queensland University of Technology Australia, Brisbane, Australia .
    Lin, Guosheng
    Australian Centre for Robotic Vision, The University of Adelaide, Australia, Adelaide, Australia.
    Liu, Fayao
    Australian Centre for Robotic Vision, The University of Adelaide, Australia, Adelaide, Australia.
    Pepperell, Edward
    Australian Centre for Robotic Vision, Queensland University of Technology Australia, Brisbane, Australia .
    Cadena, Cesar
    Australian Centre for Robotic Vision, The University of Adelaide, Australia, Adelaide, Australia.
    Upcroft, Ben
    Australian Centre for Robotic Vision, Queensland University of Technology Australia, Brisbane, Australia .
    Reid, Ian
    Australian Centre for Robotic Vision, The University of Adelaide, Australia, Adelaide, Australia.
    Sequence Searching With Deep-Learnt Depth for Condition- and Viewpoint-Invariant Route-Based Place Recognition2015In: 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, IEEE conference proceedings, 2015, p. 18-25Conference paper (Other academic)
    Abstract [en]

    Vision-based localization on robots and vehicles remains unsolved when extreme appearance change and viewpoint change are present simultaneously. The current state of the art approaches to this challenge either deal with only one of these two problems; for example FAB-MAP (viewpoint invariance) or SeqSLAM (appearance-invariance), or use extensive training within the test environment, an impractical requirement in many application scenarios. In this paper we significantly improve the viewpoint invariance of the SeqSLAM algorithm by using state-of-the-art deep learning techniques to generate synthetic viewpoints. Our approach is different to other deep learning approaches in that it does not rely on the ability of the CNN network to learn invariant features, but only to produce "good enough" depth images from day-time imagery only. We evaluate the system on a new multi-lane day-night car dataset specifically gathered to simultaneously test both appearance and viewpoint change. Results demonstrate that the use of synthetic viewpoints improves the maximum recall achieved at 100% precision by a factor of 2.2 and maximum recall by a factor of 2.7, enabling correct place recognition across multiple road lanes and significantly reducing the time between correct localizations.

  • 16.
    Norinder, Ulf
    et al.
    Örebro University, School of Science and Technology. Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Predicting Larch Casebearer damage with confidence using Yolo network models and conformal prediction2023In: Remote Sensing Letters, ISSN 2150-704X, E-ISSN 2150-7058, Vol. 14, no 10, p. 1023-1035Article in journal (Refereed)
    Abstract [en]

    This investigation shows that successful forecasting models for monitoring forest health status with respect to Larch Casebearer damages can be derived using a combination of a confidence predictor framework (Conformal Prediction) in combination with a deep learning architecture (Yolo v5). A confidence predictor framework can predict the current types of diseases used to develop the model and also provide indication of new, unseen, types or degrees of disease. The user of the models is also, at the same time, provided with reliable predictions and a well-established applicability domain for the model where such reliable predictions can and cannot be expected. Furthermore, the framework gracefully handles class imbalances without explicit over- or under-sampling or category weighting which may be of crucial importance in cases of highly imbalanced datasets. The present approach also provides indication of when insufficient information has been provided as input to the model at the level of accuracy (reliability) need by the user to make subsequent decisions based on the model predictions.

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