1,348 research outputs found
Integrating data from 3D CAD and 3D cameras for Real-Time Modeling
In a reversal of historic trends, the capital facilities industry is expressing an increasing desire for automation of equipment and construction processes. Simultaneously, the industry has become conscious that higher levels of interoperability are a key towards higher productivity and safer projects. In complex, dynamic, and rapidly changing three-dimensional (3D) environments such as facilities sites, cutting-edge 3D sensing technologies and processing algorithms are one area of development that can dramatically impact those projects factors. New 3D technologies are now being developed, with among them 3D camera. The main focus here is an investigation of the feasibility of rapidly combining and comparing – integrating – 3D sensed data (from a 3D camera) and 3D CAD data. Such a capability could improve construction quality assessment, facility aging assessment, as well as rapid environment reconstruction and construction automation. Some preliminary results are presented here. They deal with the challenge of fusing sensed and CAD data that are completely different in nature
Online Searching with an Autonomous Robot
We discuss online strategies for visibility-based searching for an object
hidden behind a corner, using Kurt3D, a real autonomous mobile robot. This task
is closely related to a number of well-studied problems. Our robot uses a
three-dimensional laser scanner in a stop, scan, plan, go fashion for building
a virtual three-dimensional environment. Besides planning trajectories and
avoiding obstacles, Kurt3D is capable of identifying objects like a chair. We
derive a practically useful and asymptotically optimal strategy that guarantees
a competitive ratio of 2, which differs remarkably from the well-studied
scenario without the need of stopping for surveying the environment. Our
strategy is used by Kurt3D, documented in a separate video.Comment: 16 pages, 8 figures, 12 photographs, 1 table, Latex, submitted for
publicatio
Distributed Localization Algorithms for Wireless Sensor Networks: From Design Methodology to Experimental Validation
Recent advances in the technology of wireless electronic devices have made possible to build
ad–hoc Wireless Sensor Networks (WSNs) using inexpensive nodes, consisting of low–power
processors, a modest amount of memory, and simple wireless transceivers. Over the last years,
many novel applications have been envisaged for distributed WSNs in the area of monitoring, communication, and control. Sensing and controlling the environment by using many
embedded devices forming a WSN often require the measured physical parameters to be associated with the position of the sensing device. As a consequence, one of the key enabling
and indispensable services in WSNs is localization (i.e., positioning).
Moreover, the design of various components of the protocol stack (e.g., routing and Medium
Access Control, MAC, algorithms) might take advantage of nodes’ location, thus resulting in
WSNs with improved performance. However, typical protocol design methodologies have
shown signiï¬cant limitations when applied to the ï¬eld of embedded systems, like WSNs. As
a matter of fact, the layered nature of typical design approaches limits their practical usefulness for the design of WSNs, where any vertical information (like, e.g., the actual node’s
position) should be efï¬ciently shared in such resource constrained devices. Among the proposed solutions to address this problem, we believe that the Platform–Based Design (PBD)
approach Sangiovanni-Vincentelli (2002), which is a relatively new methodology for the design of embedded systems, is a very promising paradigm for the efï¬cient design of WSNs
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
CASTER: a scintillator-based black hole finder probe
The primary scientific mission of the Black Hole Finder Probe (BHFP), part of the NASA Beyond Einstein program, is to survey the local Universe for black holes over a wide range of mass and accretion rate. One approach to such a survey is a hard X-ray coded-aperture imaging mission operating in the 10-600 keV energy band, a spectral range that is considered to be especially useful in the detection of black hole sources. The development of new inorganic scintillator materials provides improved performance (for example, with regards to energy resolution and timing) that is well suited to the BHFP science requirements. Detection planes formed with these materials coupled with a new generation of readout devices represent a major advancement in the performance capabilities of scintillator-based gamma cameras. Here, we discuss the Coded Aperture Survey Telescope for Energetic Radiation (CASTER), a concept that represents a BHFP based on the use of the latest scintillator technology
Study and application of motion measurement methods by means of opto-electronics systems - Studio e applicazione di metodi di misura del moto mediante sistemi opto-elettronici
This thesis addresses the problem of localizing a vehicle in unstructured environments through on-board instrumentation that does not require infrastructure modifications.
Two widely used opto-electronic systems which allow for non-contact measurements have been chosen: camera and laser range finder.
Particular attention is paid to the definition of a set of procedures for processing the environment information acquired with the instruments in order to provide both accuracy and robustness to measurement noise.
An important contribute of this work is the development of a robust and reliable algorithm for associating data that has been integrated in a graph based SLAM framework also taking into account uncertainty thus leading to an optimal vehicle motion estimation.
Moreover, the localization of the vehicle can be achieved in a generic environment since the developed global localization solution does not necessarily require the identification of landmarks in the environment, neither natural nor artificial.
Part of the work is dedicated to a thorough comparative analysis of the state-of-the-art scan matching methods in order to choose the best one to be employed in the solution pipeline.
In particular this investigation has highlighted that a dense scan matching approach can ensure good performances in many typical environments.
Several experiments in different environments, also with large scales, denote the effectiveness of the global localization system developed.
While the laser range data have been exploited for the global localization, a robust visual odometry has been investigated.
The results suggest that the use of camera can overcome the situations in which the solution achieved by the laser scanner has a low accuracy.
In particular the global localization framework can be applied also to the camera sensor, in order to perform a sensor fusion between two complementary instrumentations and so obtain a more reliable localization system.
The algorithms have been tested for 2D indoor environments, nevertheless it is expected that they are well suited also for 3D and outdoors
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
The paper presents a novel, principled approach to train recurrent neural
networks from the Reservoir Computing family that are robust to missing part of
the input features at prediction time. By building on the ensembling properties
of Dropout regularization, we propose a methodology, named DropIn, which
efficiently trains a neural model as a committee machine of subnetworks, each
capable of predicting with a subset of the original input features. We discuss
the application of the DropIn methodology in the context of Reservoir Computing
models and targeting applications characterized by input sources that are
unreliable or prone to be disconnected, such as in pervasive wireless sensor
networks and ambient intelligence. We provide an experimental assessment using
real-world data from such application domains, showing how the Dropin
methodology allows to maintain predictive performances comparable to those of a
model without missing features, even when 20\%-50\% of the inputs are not
available
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