2,866 research outputs found
RANSAC for Robotic Applications: A Survey
Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.This work has been partially funded by the Basque Government, Spain, under Research Teams Grant number IT1427-22 and under ELKARTEK LANVERSO Grant number KK-2022/00065; the Spanish Ministry of Science (MCIU), the State Research Agency (AEI), the European Regional Development Fund (FEDER), under Grant number PID2021-122402OB-C21 (MCIU/AEI/FEDER, UE); and the Spanish Ministry of Science, Innovation and Universities, under Grant FPU18/04737
Multi-view monocular pose estimation for spacecraft relative navigation
This paper presents a method of estimating the pose of a non-cooperative target for spacecraft rendezvous applications employing exclusively a monocular camera and a threedimensional model of the target. This model is used to build an offline database of prerendered keyframes with known poses. An online stage solves the model-to-image registration problem by matching two-dimensional point and edge features from the camera to the database. We apply our method to retrieve the motion of the now inoperational satellite ENVISAT. The combination of both feature types is shown to produce a robust pose solution even for large displacements respective to the keyframes which does not rely on real-time rendering, making it attractive for autonomous systems applications
Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery
A robust and fast automatic moving object detection and tracking system is
essential to characterize target object and extract spatial and temporal
information for different functionalities including video surveillance systems,
urban traffic monitoring and navigation, robotic. In this dissertation, I
present a collaborative Spatial Pyramid Context-aware moving object detection
and Tracking system. The proposed visual tracker is composed of one master
tracker that usually relies on visual object features and two auxiliary
trackers based on object temporal motion information that will be called
dynamically to assist master tracker. SPCT utilizes image spatial context at
different level to make the video tracking system resistant to occlusion,
background noise and improve target localization accuracy and robustness. We
chose a pre-selected seven-channel complementary features including RGB color,
intensity and spatial pyramid of HoG to encode object color, shape and spatial
layout information. We exploit integral histogram as building block to meet the
demands of real-time performance. A novel fast algorithm is presented to
accurately evaluate spatially weighted local histograms in constant time
complexity using an extension of the integral histogram method. Different
techniques are explored to efficiently compute integral histogram on GPU
architecture and applied for fast spatio-temporal median computations and 3D
face reconstruction texturing. We proposed a multi-component framework based on
semantic fusion of motion information with projected building footprint map to
significantly reduce the false alarm rate in urban scenes with many tall
structures. The experiments on extensive VOTC2016 benchmark dataset and aerial
video confirm that combining complementary tracking cues in an intelligent
fusion framework enables persistent tracking for Full Motion Video and Wide
Aerial Motion Imagery.Comment: PhD Dissertation (162 pages
Large-Scale Textured 3D Scene Reconstruction
Die Erstellung dreidimensionaler Umgebungsmodelle ist eine fundamentale Aufgabe im Bereich des maschinellen Sehens. Rekonstruktionen sind für eine Reihe von Anwendungen von Nutzen, wie bei der Vermessung, dem Erhalt von Kulturgütern oder der Erstellung virtueller Welten in der Unterhaltungsindustrie. Im Bereich des automatischen Fahrens helfen sie bei der Bewältigung einer Vielzahl an Herausforderungen. Dazu gehören Lokalisierung, das Annotieren großer Datensätze oder die vollautomatische Erstellung von Simulationsszenarien.
Die Herausforderung bei der 3D Rekonstruktion ist die gemeinsame Schätzung von Sensorposen und einem Umgebunsmodell. Redundante und potenziell fehlerbehaftete Messungen verschiedener Sensoren müssen in eine gemeinsame Repräsentation der Welt integriert werden, um ein metrisch und photometrisch korrektes Modell zu erhalten. Gleichzeitig muss die Methode effizient Ressourcen nutzen, um Laufzeiten zu erreichen, welche die praktische Nutzung ermöglichen.
In dieser Arbeit stellen wir ein Verfahren zur Rekonstruktion vor, das fähig ist, photorealistische 3D Rekonstruktionen großer Areale zu erstellen, die sich über mehrere Kilometer erstrecken. Entfernungsmessungen aus Laserscannern und Stereokamerasystemen werden zusammen mit Hilfe eines volumetrischen Rekonstruktionsverfahrens fusioniert. Ringschlüsse werden erkannt und als zusätzliche Bedingungen eingebracht, um eine global konsistente Karte zu erhalten. Das resultierende Gitternetz wird aus Kamerabildern texturiert, wobei die einzelnen Beobachtungen mit ihrer Güte gewichtet werden. Für eine nahtlose Erscheinung werden die unbekannten Belichtungszeiten und Parameter des optischen Systems mitgeschätzt und die Bilder entsprechend korrigiert.
Wir evaluieren unsere Methode auf synthetischen Daten, realen Sensordaten unseres Versuchsfahrzeugs und öffentlich verfügbaren Datensätzen. Wir zeigen qualitative Ergebnisse großer innerstädtischer Bereiche, sowie quantitative Auswertungen der Fahrzeugtrajektorie und der Rekonstruktionsqualität.
Zuletzt präsentieren wir mehrere Anwendungen und zeigen somit den Nutzen unserer Methode für Anwendungen im Bereich des automatischen Fahrens
CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics
Advances in optical and electrophysiological recording technologies have made
it possible to record the dynamics of thousands of neurons, opening up new
possibilities for interpreting and controlling large neural populations in
behaving animals. A promising way to extract computational principles from
these large datasets is to train data-constrained recurrent neural networks
(dRNNs). Performing this training in real-time could open doors for research
techniques and medical applications to model and control interventions at
single-cell resolution and drive desired forms of animal behavior. However,
existing training algorithms for dRNNs are inefficient and have limited
scalability, making it a challenge to analyze large neural recordings even in
offline scenarios. To address these issues, we introduce a training method
termed Convex Optimization of Recurrent Neural Networks (CORNN). In studies of
simulated recordings, CORNN attained training speeds ~100-fold faster than
traditional optimization approaches while maintaining or enhancing modeling
accuracy. We further validated CORNN on simulations with thousands of cells
that performed simple computations such as those of a 3-bit flip-flop or the
execution of a timed response. Finally, we showed that CORNN can robustly
reproduce network dynamics and underlying attractor structures despite
mismatches between generator and inference models, severe subsampling of
observed neurons, or mismatches in neural time-scales. Overall, by training
dRNNs with millions of parameters in subminute processing times on a standard
computer, CORNN constitutes a first step towards real-time network reproduction
constrained on large-scale neural recordings and a powerful computational tool
for advancing the understanding of neural computation.Comment: Accepted at NeurIPS 202
An interactive ImageJ plugin for semi-automated image denoising in electron microscopy
The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
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Tools for the Development of Advanced Thermal Management Techniques for Future Safety-Critical Embedded Systems
Softwarové metody snižování teploty ukazují velký potenciál pro výpočetní platformy malých letadel pro avioniku tím, že umožňují zvýšenou spolehlivost, výkon a zmenšení velikosti a hmotnosti bez zvýšení nákladů na hardware. Pro vyhodnocení těchto metod uvádíme dvojici nástrojů pro záznam a zpracování dat z teplotních senzorů a z termální kamery při různých pracovních zatížení.. Tyto nástroje jsou pak použité k lokalizaci zdrojů tepla na čipu a k navrhování metod pro snižování teploty čipů. Nástroje splňují jejich požadavky a jsou úspěšně použité pro vyhodnocení metod snižování teploty.Software-based temperature reduction methods show great potential for small aircraft avionics computing platforms by allowing improved dependability, performance and reduction in size and weight without increasing hardware costs. To evaluate such methods, we present a pair of tools for recording and processing data from temperature sensors and a thermal camera during the execution of various workloads. These tools are then used to determine locations of on-chip heat sources and to propose methods for reducing chip temperature. The tools meet their requirements and are successfully used for the evaluation of temperature reduction methods
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