14,401 research outputs found
Mesh-based 3D Textured Urban Mapping
In the era of autonomous driving, urban mapping represents a core step to let
vehicles interact with the urban context. Successful mapping algorithms have
been proposed in the last decade building the map leveraging on data from a
single sensor. The focus of the system presented in this paper is twofold: the
joint estimation of a 3D map from lidar data and images, based on a 3D mesh,
and its texturing. Indeed, even if most surveying vehicles for mapping are
endowed by cameras and lidar, existing mapping algorithms usually rely on
either images or lidar data; moreover both image-based and lidar-based systems
often represent the map as a point cloud, while a continuous textured mesh
representation would be useful for visualization and navigation purposes. In
the proposed framework, we join the accuracy of the 3D lidar data, and the
dense information and appearance carried by the images, in estimating a
visibility consistent map upon the lidar measurements, and refining it
photometrically through the acquired images. We evaluate the proposed framework
against the KITTI dataset and we show the performance improvement with respect
to two state of the art urban mapping algorithms, and two widely used surface
reconstruction algorithms in Computer Graphics.Comment: accepted at iros 201
Active SLAM for autonomous underwater exploration
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version
Mapping Exoplanets
The varied surfaces and atmospheres of planets make them interesting places
to live, explore, and study from afar. Unfortunately, the great distance to
exoplanets makes it impossible to resolve their disk with current or near-term
technology. It is still possible, however, to deduce spatial inhomogeneities in
exoplanets provided that different regions are visible at different
times---this can be due to rotation, orbital motion, and occultations by a
star, planet, or moon. Astronomers have so far constructed maps of thermal
emission and albedo for short period giant planets. These maps constrain
atmospheric dynamics and cloud patterns in exotic atmospheres. In the future,
exo-cartography could yield surface maps of terrestrial planets, hinting at the
geophysical and geochemical processes that shape them.Comment: Updated chapter for Handbook of Exoplanets, eds. Deeg & Belmonte. 17
pages, including 6 figures and 4 pages of reference
Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation
The monocular vision-based simultaneous localization and mapping (vSLAM) is
one of the most challenging problem in mobile robotics and computer vision. In
this work we study the post-processing techniques applied to sparse 3D
point-cloud maps, obtained by feature-based vSLAM algorithms. Map
post-processing is split into 2 major steps: 1) noise and outlier removal and
2) upsampling. We evaluate different combinations of known algorithms for
outlier removing and upsampling on datasets of real indoor and outdoor
environments and identify the most promising combination. We further use it to
convert a point-cloud map, obtained by the real UAV performing indoor flight to
3D voxel grid (octo-map) potentially suitable for path planning.Comment: 10 pages, 4 figures, camera-ready version of paper for "The 3rd
International Conference on Interactive Collaborative Robotics (ICR 2018)
SMAT: Simultaneous and Self-Reinforced Mapping and Tracking in Dynamic Urban Scenariosorcing Framework for Simultaneous Mapping and Tracking in Unbounded Urban Environments
Despite the increasing prevalence of robots in daily life, their navigation
capabilities are still limited to environments with prior knowledge, such as a
global map. To fully unlock the potential of robots, it is crucial to enable
them to navigate in large-scale unknown and changing unstructured scenarios.
This requires the robot to construct an accurate static map in real-time as it
explores, while filtering out moving objects to ensure mapping accuracy and, if
possible, achieving high-quality pedestrian tracking and collision avoidance.
While existing methods can achieve individual goals of spatial mapping or
dynamic object detection and tracking, there has been limited research on
effectively integrating these two tasks, which are actually coupled and
reciprocal. In this work, we propose a solution called SMAT (Simultaneous
and Self-Reinforced Mapping and Tracking) that integrates a front-end dynamic
object detection and tracking module with a back-end static mapping module.
SMAT leverages the close and reciprocal interplay between these two modules
to efficiently and effectively solve the open problem of simultaneous tracking
and mapping in highly dynamic scenarios. We conducted extensive experiments
using widely-used datasets and simulations, providing both qualitative and
quantitative results to demonstrate SMAT's state-of-the-art performance in
dynamic object detection, tracking, and high-quality static structure mapping.
Additionally, we performed long-range robotic navigation in real-world urban
scenarios spanning over 7 km, which included challenging obstacles like
pedestrians and other traffic agents. The successful navigation provides a
comprehensive test of SMAT's robustness, scalability, efficiency, quality,
and its ability to benefit autonomous robots in wild scenarios without
pre-built maps.Comment: homepage: https://sites.google.com/view/smat-na
A Multi-Sensor Fusion-Based Underwater Slam System
This dissertation addresses the problem of real-time Simultaneous Localization and Mapping (SLAM) in challenging environments. SLAM is one of the key enabling technologies for autonomous robots to navigate in unknown environments by processing information on their on-board computational units. In particular, we study the exploration of challenging GPS-denied underwater environments to enable a wide range of robotic applications, including historical studies, health monitoring of coral reefs, underwater infrastructure inspection e.g., bridges, hydroelectric dams, water supply systems, and oil rigs. Mapping underwater structures is important in several fields, such as marine archaeology, Search and Rescue (SaR), resource management, hydrogeology, and speleology. However, due to the highly unstructured nature of such environments, navigation by human divers could be extremely dangerous, tedious, and labor intensive. Hence, employing an underwater robot is an excellent fit to build the map of the environment while simultaneously localizing itself in the map.
The main contribution of this dissertation is the design and development of a real-time robust SLAM algorithm for small and large scale underwater environments. SVIn – a novel tightly-coupled keyframe-based non-linear optimization framework fusing Sonar, Visual, Inertial and water depth information with robust initialization, loop-closing, and relocalization capabilities has been presented. Introducing acoustic range information to aid the visual data, shows improved reconstruction and localization. The availability of depth information from water pressure enables a robust initialization and refines the scale factor, as well as assists to reduce the drift for the tightly-coupled integration. The complementary characteristics of these sensing v modalities provide accurate and robust localization in unstructured environments with low visibility and low visual features – as such make them the ideal choice for underwater navigation. The proposed system has been successfully tested and validated in both benchmark datasets and numerous real world scenarios. It has also been used for planning for underwater robot in the presence of obstacles. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle (AUV) Aqua2 in challenging underwater environments with poor visibility, demonstrate performance never achieved before in terms of accuracy and robustness. To aid the sparse reconstruction, a contour-based reconstruction approach utilizing the well defined edges between the well lit area and darkness has been developed. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures and the shadow contours. The proposed method utilizes these contours to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a visual odometry system. Experimental results in an underwater cave demonstrate the performance of our system. This enables more robust navigation of autonomous underwater vehicles using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions
Using airborne LiDAR Survey to explore historic-era archaeological landscapes of Montserrat in the eastern Caribbean
This article describes what appears to be the first archaeological application of airborne LiDAR survey to historic-era landscapes in the Caribbean archipelago, on the island of Montserrat. LiDAR is proving invaluable in extending the reach of traditional pedestrian survey into less favorable areas, such as those covered by dense neotropical forest and by ashfall from the past two decades of active eruptions by the Soufrière Hills volcano, and to sites in localities that are inaccessible on account of volcanic dangers. Emphasis is placed on two aspects of the research: first, the importance of ongoing, real-time interaction between the LiDAR analyst and the archaeological team in the field; and second, the advantages of exploiting the full potential of the three-dimensional LiDAR point cloud data for purposes of the visualization of archaeological sites and features
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