3,964 research outputs found
A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data
In this paper we present a practical approach for generating an
occlusion-free textured 3D map of urban facades by the synergistic use of
terrestrial images, 3D point clouds and area-based information. Particularly in
dense urban environments, the high presence of urban objects in front of the
facades causes significant difficulties for several stages in computational
building modeling. Major challenges lie on the one hand in extracting complete
3D facade quadrilateral delimitations and on the other hand in generating
occlusion-free facade textures. For these reasons, we describe a
straightforward approach for completing and recovering facade geometry and
textures by exploiting the data complementarity of terrestrial multi-source
imagery and area-based information
Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping
The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles
Autonomous driving (AD) perception today relies heavily on deep learning
based architectures requiring large scale annotated datasets with their
associated costs for curation and annotation. The 3D semantic data are useful
for core perception tasks such as obstacle detection and ego-vehicle
localization. We propose a new dataset, Navya 3D Segmentation (Navya3DSeg),
with a diverse label space corresponding to a large scale production grade
operational domain, including rural, urban, industrial sites and universities
from 13 countries. It contains 23 labeled sequences and 25 supplementary
sequences without labels, designed to explore self-supervised and
semi-supervised semantic segmentation benchmarks on point clouds. We also
propose a novel method for sequential dataset split generation based on
iterative multi-label stratification, and demonstrated to achieve a +1.2% mIoU
improvement over the original split proposed by SemanticKITTI dataset. A
complete benchmark for semantic segmentation task was performed, with state of
the art methods. Finally, we demonstrate an active learning (AL) based dataset
distillation framework. We introduce a novel heuristic-free sampling method
called distance sampling in the context of AL. A detailed presentation on the
dataset is available at https://www.youtube.com/watch?v=5m6ALIs-s20 .Comment: Submitted to RA-L. Version with supplementary material
Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities
With recent advances in artificial intelligence (AI) and robotics, unmanned
vehicle swarms have received great attention from both academia and industry
due to their potential to provide services that are difficult and dangerous to
perform by humans. However, learning and coordinating movements and actions for
a large number of unmanned vehicles in complex and dynamic environments
introduce significant challenges to conventional AI methods. Generative AI
(GAI), with its capabilities in complex data feature extraction,
transformation, and enhancement, offers great potential in solving these
challenges of unmanned vehicle swarms. For that, this paper aims to provide a
comprehensive survey on applications, challenges, and opportunities of GAI in
unmanned vehicle swarms. Specifically, we first present an overview of unmanned
vehicles and unmanned vehicle swarms as well as their use cases and existing
issues. Then, an in-depth background of various GAI techniques together with
their capabilities in enhancing unmanned vehicle swarms are provided. After
that, we present a comprehensive review on the applications and challenges of
GAI in unmanned vehicle swarms with various insights and discussions. Finally,
we highlight open issues of GAI in unmanned vehicle swarms and discuss
potential research directions.Comment: 23 page
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