8,845 research outputs found
Predicting the Next Best View for 3D Mesh Refinement
3D reconstruction is a core task in many applications such as robot
navigation or sites inspections. Finding the best poses to capture part of the
scene is one of the most challenging topic that goes under the name of Next
Best View. Recently, many volumetric methods have been proposed; they choose
the Next Best View by reasoning over a 3D voxelized space and by finding which
pose minimizes the uncertainty decoded into the voxels. Such methods are
effective, but they do not scale well since the underlaying representation
requires a huge amount of memory. In this paper we propose a novel mesh-based
approach which focuses on the worst reconstructed region of the environment
mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and
an energy function over the worst regions of the mesh which takes into account
the mutual parallax with respect to the previous cameras, the angle of
incidence of the viewing ray to the surface and the visibility of the region.
We test our approach over a well known dataset and achieve state-of-the-art
results.Comment: 13 pages, 5 figures, to be published in IAS-1
Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study
A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411
Vision-model-based Real-time Localization of Unmanned Aerial Vehicle for Autonomous Structure Inspection under GPS-denied Environment
UAVs have been widely used in visual inspections of buildings, bridges and
other structures. In either outdoor autonomous or semi-autonomous flights
missions strong GPS signal is vital for UAV to locate its own positions.
However, strong GPS signal is not always available, and it can degrade or fully
loss underneath large structures or close to power lines, which can cause
serious control issues or even UAV crashes. Such limitations highly restricted
the applications of UAV as a routine inspection tool in various domains. In
this paper a vision-model-based real-time self-positioning method is proposed
to support autonomous aerial inspection without the need of GPS support.
Compared to other localization methods that requires additional onboard
sensors, the proposed method uses a single camera to continuously estimate the
inflight poses of UAV. Each step of the proposed method is discussed in detail,
and its performance is tested through an indoor test case.Comment: 8 pages, 5 figures, submitted to i3ce 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
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