91 research outputs found
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes
Robotic Information Gathering (RIG) is a foundational research topic that
answers how a robot (team) collects informative data to efficiently build an
accurate model of an unknown target function under robot embodiment
constraints. RIG has many applications, including but not limited to autonomous
exploration and mapping, 3D reconstruction or inspection, search and rescue,
and environmental monitoring. A RIG system relies on a probabilistic model's
prediction uncertainty to identify critical areas for informative data
collection. Gaussian Processes (GPs) with stationary kernels have been widely
adopted for spatial modeling. However, real-world spatial data is typically
non-stationary -- different locations do not have the same degree of
variability. As a result, the prediction uncertainty does not accurately reveal
prediction error, limiting the success of RIG algorithms. We propose a family
of non-stationary kernels named Attentive Kernel (AK), which is simple, robust,
and can extend any existing kernel to a non-stationary one. We evaluate the new
kernel in elevation mapping tasks, where AK provides better accuracy and
uncertainty quantification over the commonly used stationary kernels and the
leading non-stationary kernels. The improved uncertainty quantification guides
the downstream informative planner to collect more valuable data around the
high-error area, further increasing prediction accuracy. A field experiment
demonstrates that the proposed method can guide an Autonomous Surface Vehicle
(ASV) to prioritize data collection in locations with significant spatial
variations, enabling the model to characterize salient environmental features.Comment: International Journal of Robotics Research (IJRR). arXiv admin note:
text overlap with arXiv:2205.0642
Improving Self-Consistency in Underwater Mapping Through Laser-Based Loop Closure (Extended)
Accurate, self-consistent bathymetric maps are needed to monitor changes in
subsea environments and infrastructure. These maps are increasingly collected
by underwater vehicles, and mapping requires an accurate vehicle navigation
solution. Commercial off-the-shelf (COTS) navigation solutions for underwater
vehicles often rely on external acoustic sensors for localization, however
survey-grade acoustic sensors are expensive to deploy and limit the range of
the vehicle. Techniques from the field of simultaneous localization and
mapping, particularly loop closures, can improve the quality of the navigation
solution over dead-reckoning, but are difficult to integrate into COTS
navigation systems. This work presents a method to improve the self-consistency
of bathymetric maps by smoothly integrating loop-closure measurements into the
state estimate produced by a commercial subsea navigation system. Integration
is done using a white-noise-on-acceleration motion prior, without access to raw
sensor measurements or proprietary models. Improvements in map self-consistency
are shown for both simulated and experimental datasets, including a 3D scan of
an underwater shipwreck in Wiarton, Ontario, Canada.Comment: 26 pages, 18 figures. V2 correct Table III x2 parameter values, Table
VIII 'INS' values, and equation A.2
VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts
Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9
Workshops Stream 1 10
Workshop Stream 2 11
Workshop Stream 3 12
Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14
Session 2 – Visualisation, communication & Teaching 27
Session 3 – Applying Machine Learning in Geosciences 36
Session 4 – Digital Outcrop Characterisation & Analysis 49
Session 5 – Airborne & Remote Mapping 58
Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69
Session 7 – Applications in Hydrology & Ecology 82
Poster Contributions 9
Localization, Mapping and SLAM in Marine and Underwater Environments
The use of robots in marine and underwater applications is growing rapidly. These applications share the common requirement of modeling the environment and estimating the robots’ pose. Although there are several mapping, SLAM, target detection and localization methods, marine and underwater environments have several challenging characteristics, such as poor visibility, water currents, communication issues, sonar inaccuracies or unstructured environments, that have to be considered. The purpose of this Special Issue is to present the current research trends in the topics of underwater localization, mapping, SLAM, and target detection and localization. To this end, we have collected seven articles from leading researchers in the field, and present the different approaches and methods currently being investigated to improve the performance of underwater robots
UAVs for the Environmental Sciences
This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application
Underwater Motion Estimation Based on Acoustic Images and Deep Learning
This work develops techniques to estimate the motion of an underwater vehicle by processing acoustic images using deep learning (DL). For this, an underwater sonar simulator based on ray-tracing is designed and implemented. The simulator provides the ground truth data to train and validate proposed techniques. Several DL networks are implemented and compared to identify the most suitable for motion estimation using sonar images. The DL methods showed a much lower computation time and more accurate motion estimates compared to a deterministic algorithm. Further improvements of the DL methods are investigated by preprocessing the data before feeding it to the DL network. One technique converts sonar images into vectors by adding up the pixels in each row. This reduces the size of the DL networks. This technique showed significant reduction in the computation time of up to 10 times compared to techniques that use images. Another preprocessing technique divides the field of view (FoV) of a simulated sonar into four quadrants. An image is generated from each quadrant. This is combined with the vector technique by converting the images into vectors and grouping them together as the input of the DL network. The FoV division approach showed a high accuracy compared to using the whole FoV or different portions of it. Another motion estimation method presented in this work is enabled by full-duplex operation and rather than using images, it is based on DL analysis of time variation of complex-valued channel impulse responses. This technique can significantly reduce the acoustic hardware and processing complexity of the DL network and obtain a higher motion estimation accuracy, compared with techniques based on the processing of sonar images. The navigation accuracy of all the techniques is further illustrated by examples of estimation of complex trajectories using simulated and real data
Deep learning for internet of underwater things and ocean data analytics
The Internet of Underwater Things (IoUT) is an emerging technological ecosystem developed for connecting objects in maritime and underwater environments. IoUT technologies are empowered by an extreme number of deployed sensors and actuators. In this thesis, multiple IoUT sensory data are augmented with machine intelligence for forecasting purposes
Pathways to spatial cognition : a multi-domain approach SpatialTrain I
“Opening a window into the future is not an easy task. Attempting to open one in a generation after the initial launching step might seemed either idealistic, naïve or with hindsight plain driven” (Formosa, 2017, p35). The drive to introduce Spatial Information integration across the Maltese Islands was an ideal, one that brought in technology, methodologies and results. However, as in the classic GIS evolution through the decades pointers on what constitutes a spatial information system were the subject of extensive debate Initially this was driven by the Push – Pull factor where entities using the primitive systems were being pushed by the availability of a mapping system and provision of base maps and hence creating data to fit the system. Initiated in the 1960s through military use, porting the processes to the physical and urban domains in the 1980s and 1990s, further takeup was made in the environmental domains in the 1990s to 2000s and eventually to the social domain in the 2000 to 2010s. Jumping through the decades, the global explosion of GIS and Spatial awareness as well as software, methods and integrative constructs morphed GIS into an availability that made it all possible, particularly through online and web-enabled GIS. This Pull – Push factor caused entities and private organisations to finally break through by creating their own data and then going for the mapping systems that fit their needs, systems that have evolved beyond recognition, both in the proprietary and open-source/open-access arenas. [Excerpt from the Introduction by Prof. Saviour Formosa]peer-reviewe
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