6,802 research outputs found
Furniture models learned from the WWW: using web catalogs to locate and categorize unknown furniture pieces in 3D laser scans
In this article, we investigate how autonomous robots can exploit the high quality information already available from the WWW concerning 3-D models of office furniture. Apart from the hobbyist effort in Google 3-D Warehouse, many companies providing office furnishings already have the models for considerable portions of the objects found in our workplaces and homes. In particular, we present an approach that allows a robot to learn generic models of typical office furniture using examples found in the Web. These generic models are then used by the robot to locate and categorize unknown furniture in real indoor environments
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation
Appearance changes due to weather and seasonal conditions represent a strong
impediment to the robust implementation of machine learning systems in outdoor
robotics. While supervised learning optimises a model for the training domain,
it will deliver degraded performance in application domains that underlie
distributional shifts caused by these changes. Traditionally, this problem has
been addressed via the collection of labelled data in multiple domains or by
imposing priors on the type of shift between both domains. We frame the problem
in the context of unsupervised domain adaptation and develop a framework for
applying adversarial techniques to adapt popular, state-of-the-art network
architectures with the additional objective to align features across domains.
Moreover, as adversarial training is notoriously unstable, we first perform an
extensive ablation study, adapting many techniques known to stabilise
generative adversarial networks, and evaluate on a surrogate classification
task with the same appearance change. The distilled insights are applied to the
problem of free-space segmentation for motion planning in autonomous driving.Comment: In Proceedings of the 2017 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2017
Cumulative object categorization in clutter
In this paper we present an approach based on scene- or part-graphs for geometrically categorizing touching and
occluded objects. We use additive RGBD feature descriptors and hashing of graph configuration parameters for describing the spatial arrangement of constituent parts. The presented experiments quantify that this method outperforms our earlier part-voting and sliding window classification. We evaluated our approach on cluttered scenes, and by using a 3D dataset containing over 15000 Kinect scans of over 100 objects which were grouped into general geometric categories. Additionally, color, geometric, and combined features were compared for categorization tasks
Sparsity Invariant CNNs
In this paper, we consider convolutional neural networks operating on sparse
inputs with an application to depth upsampling from sparse laser scan data.
First, we show that traditional convolutional networks perform poorly when
applied to sparse data even when the location of missing data is provided to
the network. To overcome this problem, we propose a simple yet effective sparse
convolution layer which explicitly considers the location of missing data
during the convolution operation. We demonstrate the benefits of the proposed
network architecture in synthetic and real experiments with respect to various
baseline approaches. Compared to dense baselines, the proposed sparse
convolution network generalizes well to novel datasets and is invariant to the
level of sparsity in the data. For our evaluation, we derive a novel dataset
from the KITTI benchmark, comprising 93k depth annotated RGB images. Our
dataset allows for training and evaluating depth upsampling and depth
prediction techniques in challenging real-world settings and will be made
available upon publication
Developing a collaborative HBIM to integrate tangible and intangible cultural heritage
This is an accepted manuscript of an article published by Emerald in International Journal of Building Pathology and Adaptation on 21/03/2020, available online at: https://doi.org/10.1108/IJBPA-04-2019-0036
The accepted version of the publication may differ from the final published version.Purpose: The purpose of this paper is to report on the development of a collaborative Heritage BIM (HBIM) of a 19th Century multi-building industrial site in the UK. The buildings were Grade II listed by Historic England for architectural and structural features. The buildings were also a key element of the industrial heritage and folklore of the surrounding area. As the site was due to undergo major renovation work, this project was initiated to develop a HBIM of the site that encapsulated both tangible and intangible heritage data.
Design/methodology/approach: The design of the research in this study combined multiple research methods. Building on an analysis of secondary data surrounding HBIM, a Community of Practice (CoP) was established to shape the development of a Heritage BIM Execution Plan (HBEP) and underpin the collaborative BIM development. The tangible HBIM geometry was predominantly developed using a scan to BIM methodology, whereas intangible heritage data was undertaken using unstructured interviews and a focus group used to inform the presentation approach of the HBIM data.
Findings: The project produced a collaboratively generated multi-building Heritage BIM. The study identified the need for a dedicated HBEP which varies from prevailing BEPs on construction projects. Tangible geometry of the buildings were modelled to LOD3 of the Historic England guidelines. Notably, the work identified the fluid nature of intangible data and the need to include this in a HBIM to fully support design, construction and operation of the building after renovation. A methodology was implemented to categorise intangible heritage data within a BIM context and an approach to interrogate this data from within existing BIM software tools.
Originality/Value: The work has presented an approach to the development of HBIM for large sites containing multiple buildings/assets. The framework implemented for a HBEP can be reproduced by future researchers and practitioners wishing to undertake similar projects. The method for identifying and categorising intangible heritage information through the developed Level of Intangible Cultural Heritage (LOICH), was presented as new knowledge. The development of HBIM to bring together tangible and intangible data has the potential to provide a model for future work in the field and augment existing BIM data sets used during the asset lifecycle
Lidar-based Obstacle Detection and Recognition for Autonomous Agricultural Vehicles
Today, agricultural vehicles are available that can drive autonomously and follow exact route plans more precisely than human operators. Combined with advancements in precision agriculture, autonomous agricultural robots can reduce manual labor, improve workflow, and optimize yield. However, as of today, human operators are still required for monitoring the environment and acting upon potential obstacles in front of the vehicle. To eliminate this need, safety must be ensured by accurate and reliable obstacle detection and avoidance systems.In this thesis, lidar-based obstacle detection and recognition in agricultural environments has been investigated. A rotating multi-beam lidar generating 3D point clouds was used for point-wise classification of agricultural scenes, while multi-modal fusion with cameras and radar was used to increase performance and robustness. Two research perception platforms were presented and used for data acquisition. The proposed methods were all evaluated on recorded datasets that represented a wide range of realistic agricultural environments and included both static and dynamic obstacles.For 3D point cloud classification, two methods were proposed for handling density variations during feature extraction. One method outperformed a frequently used generic 3D feature descriptor, whereas the other method showed promising preliminary results using deep learning on 2D range images. For multi-modal fusion, four methods were proposed for combining lidar with color camera, thermal camera, and radar. Gradual improvements in classification accuracy were seen, as spatial, temporal, and multi-modal relationships were introduced in the models. Finally, occupancy grid mapping was used to fuse and map detections globally, and runtime obstacle detection was applied on mapped detections along the vehicle path, thus simulating an actual traversal.The proposed methods serve as a first step towards full autonomy for agricultural vehicles. The study has thus shown that recent advancements in autonomous driving can be transferred to the agricultural domain, when accurate distinctions are made between obstacles and processable vegetation. Future research in the domain has further been facilitated with the release of the multi-modal obstacle dataset, FieldSAFE
A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing
This work introduces an innovative parallel, fully-distributed finite element
framework for growing geometries and its application to metal additive
manufacturing. It is well-known that virtual part design and qualification in
additive manufacturing requires highly-accurate multiscale and multiphysics
analyses. Only high performance computing tools are able to handle such
complexity in time frames compatible with time-to-market. However, efficiency,
without loss of accuracy, has rarely held the centre stage in the numerical
community. Here, in contrast, the framework is designed to adequately exploit
the resources of high-end distributed-memory machines. It is grounded on three
building blocks: (1) Hierarchical adaptive mesh refinement with octree-based
meshes; (2) a parallel strategy to model the growth of the geometry; (3)
state-of-the-art parallel iterative linear solvers. Computational experiments
consider the heat transfer analysis at the part scale of the printing process
by powder-bed technologies. After verification against a 3D benchmark, a
strong-scaling analysis assesses performance and identifies major sources of
parallel overhead. A third numerical example examines the efficiency and
robustness of (2) in a curved 3D shape. Unprecedented parallelism and
scalability were achieved in this work. Hence, this framework contributes to
take on higher complexity and/or accuracy, not only of part-scale simulations
of metal or polymer additive manufacturing, but also in welding, sedimentation,
atherosclerosis, or any other physical problem where the physical domain of
interest grows in time
AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-time High-Fidelity LiDAR Simulation
LiDAR sensors provide rich 3D information about their surrounding and are
becoming increasingly important for autonomous vehicles tasks, such as semantic
segmentation, object detection, and tracking. Simulating a LiDAR sensor
accelerates the testing, validation, and deployment of autonomous vehicles,
while reducing the cost and eliminating the risks of testing in real-world
scenarios. We address the problem of high-fidelity LiDAR simulation and present
a pipeline that leverages real-world point clouds acquired by mobile mapping
systems. Point-based geometry representations, more specifically splats, have
proven their ability to accurately model the underlying surface in very large
point clouds. We introduce an adaptive splats generation method that accurately
models the underlying 3D geometry, especially for thin structures. Moreover, we
introduce a physics-based, faster-than-real-time LiDAR simulator, in the
splatted model, leveraging the GPU parallel architecture with an acceleration
structure, while focusing on efficiently handling large point clouds. We test
our LiDAR simulation in real-world conditions, showing qualitative and
quantitative results compared to basic splatting and meshing techniques,
demonstrating the interest of our modeling technique.Comment: 28 pages, 11 figures, 6 table
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