674 research outputs found
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras
Visual scene understanding is an important capability that enables robots to
purposefully act in their environment. In this paper, we propose a novel
approach to object-class segmentation from multiple RGB-D views using deep
learning. We train a deep neural network to predict object-class semantics that
is consistent from several view points in a semi-supervised way. At test time,
the semantics predictions of our network can be fused more consistently in
semantic keyframe maps than predictions of a network trained on individual
views. We base our network architecture on a recent single-view deep learning
approach to RGB and depth fusion for semantic object-class segmentation and
enhance it with multi-scale loss minimization. We obtain the camera trajectory
using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth
annotated frames in order to enforce multi-view consistency during training. At
test time, predictions from multiple views are fused into keyframes. We propose
and analyze several methods for enforcing multi-view consistency during
training and testing. We evaluate the benefit of multi-view consistency
training and demonstrate that pooling of deep features and fusion over multiple
views outperforms single-view baselines on the NYUDv2 benchmark for semantic
segmentation. Our end-to-end trained network achieves state-of-the-art
performance on the NYUDv2 dataset in single-view segmentation as well as
multi-view semantic fusion.Comment: the 2017 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2017
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Resilient Perception for Outdoor Unmanned Ground Vehicles
This thesis promotes the development of resilience for perception systems with a focus on Unmanned Ground Vehicles (UGVs) in adverse environmental conditions. Perception is the interpretation of sensor data to produce a representation of the environment that is necessary for subsequent decision making. Long-term autonomy requires perception systems that correctly function in unusual but realistic conditions that will eventually occur during extended missions. State-of-the-art UGV systems can fail when the sensor data are beyond the operational capacity of the perception models. The key to resilient perception system lies in the use of multiple sensor modalities and the pre-selection of appropriate sensor data to minimise the chance of failure. This thesis proposes a framework based on diagnostic principles to evaluate and preselect sensor data prior to interpretation by the perception system. Image-based quality metrics are explored and evaluated experimentally using infrared (IR) and visual cameras onboard a UGV in the presence of smoke and airborne dust. A novel quality metric, Spatial Entropy (SE), is introduced and evaluated. The proposed framework is applied to a state-of-the-art Visual-SLAM algorithm combining visual and IR imaging as a real-world example. An extensive experimental evaluation demonstrates that the framework allows for camera-based localisation that is resilient to a range of low-visibility conditions when compared to other methods that use a single sensor or combine sensor data without selection. The proposed framework allows for a resilient localisation in adverse conditions using image data but also has significant potential to benefit many perception applications. Employing multiple sensing modalities along with pre-selection of appropriate data is a powerful method to create resilient perception systems by anticipating and mitigating errors. The development of such resilient perception systems is a requirement for next-generation outdoor UGVs
Image-based 3-D reconstruction of constrained environments
Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions.Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions
Proceedings of the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
This book is a collection of 15 reviewed technical reports summarizing the presentations at the 2011 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. The covered topics include image processing, optical signal processing, visual inspection, pattern recognition and classification, human-machine interaction, world and situation modeling, autonomous system localization and mapping, information fusion, and trust propagation in sensor networks
LiDAR-Based Place Recognition For Autonomous Driving: A Survey
LiDAR-based place recognition (LPR) plays a pivotal role in autonomous
driving, which assists Simultaneous Localization and Mapping (SLAM) systems in
reducing accumulated errors and achieving reliable localization. However,
existing reviews predominantly concentrate on visual place recognition (VPR)
methods. Despite the recent remarkable progress in LPR, to the best of our
knowledge, there is no dedicated systematic review in this area. This paper
bridges the gap by providing a comprehensive review of place recognition
methods employing LiDAR sensors, thus facilitating and encouraging further
research. We commence by delving into the problem formulation of place
recognition, exploring existing challenges, and describing relations to
previous surveys. Subsequently, we conduct an in-depth review of related
research, which offers detailed classifications, strengths and weaknesses, and
architectures. Finally, we summarize existing datasets, commonly used
evaluation metrics, and comprehensive evaluation results from various methods
on public datasets. This paper can serve as a valuable tutorial for newcomers
entering the field of place recognition and for researchers interested in
long-term robot localization. We pledge to maintain an up-to-date project on
our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table
Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching
Successful visual navigation depends upon capturing images that contain
sufficient useful information. In this paper, we explore a data-driven approach
to account for environmental lighting changes, improving the quality of images
for use in visual odometry (VO) or visual simultaneous localization and mapping
(SLAM). We train a deep convolutional neural network model to predictively
adjust camera gain and exposure time parameters such that consecutive images
contain a maximal number of matchable features. The training process is fully
self-supervised: our training signal is derived from an underlying VO or SLAM
pipeline and, as a result, the model is optimized to perform well with that
specific pipeline. We demonstrate through extensive real-world experiments that
our network can anticipate and compensate for dramatic lighting changes (e.g.,
transitions into and out of road tunnels), maintaining a substantially higher
number of inlier feature matches than competing camera parameter control
algorithms.Comment: Accepted to IEEE Robotics and Automation Letters and to the IEEE
International Conference on Robotics and Automation (ICRA) 202
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