3,465 research outputs found
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud
representation of the scene that does not model the topology of the
environment. A 3D mesh instead offers a richer, yet lightweight, model.
Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks
triangulated by a VIO algorithm often results in a mesh that does not fit the
real scene. In order to regularize the mesh, previous approaches decouple state
estimation from the 3D mesh regularization step, and either limit the 3D mesh
to the current frame or let the mesh grow indefinitely. We propose instead to
tightly couple mesh regularization and state estimation by detecting and
enforcing structural regularities in a novel factor-graph formulation. We also
propose to incrementally build the mesh by restricting its extent to the
time-horizon of the VIO optimization; the resulting 3D mesh covers a larger
portion of the scene than a per-frame approach while its memory usage and
computational complexity remain bounded. We show that our approach successfully
regularizes the mesh, while improving localization accuracy, when structural
regularities are present, and remains operational in scenes without
regularities.Comment: 7 pages, 5 figures, ICRA accepte
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
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
View Selection with Geometric Uncertainty Modeling
Estimating positions of world points from features observed in images is a
key problem in 3D reconstruction, image mosaicking,simultaneous localization
and mapping and structure from motion. We consider a special instance in which
there is a dominant ground plane viewed from a parallel viewing
plane above it. Such instances commonly arise, for example, in
aerial photography. Consider a world point and its worst
case reconstruction uncertainty obtained by
merging \emph{all} possible views of chosen from . We first
show that one can pick two views and such that the uncertainty
obtained using only these two views is almost as
good as (i.e. within a small constant factor of) .
Next, we extend the result to the entire ground plane and show
that one can pick a small subset of (which
grows only linearly with the area of ) and still obtain a constant
factor approximation, for every point , to the minimum worst
case estimate obtained by merging all views in . Finally, we
present a multi-resolution view selection method which extends our techniques
to non-planar scenes. We show that the method can produce rich and accurate
dense reconstructions with a small number of views. Our results provide a view
selection mechanism with provable performance guarantees which can drastically
increase the speed of scene reconstruction algorithms. In addition to
theoretical results, we demonstrate their effectiveness in an application where
aerial imagery is used for monitoring farms and orchards
Performance Evaluation of Vision-Based Algorithms for MAVs
An important focus of current research in the field of Micro Aerial Vehicles
(MAVs) is to increase the safety of their operation in general unstructured
environments. Especially indoors, where GPS cannot be used for localization,
reliable algorithms for localization and mapping of the environment are
necessary in order to keep an MAV airborne safely. In this paper, we compare
vision-based real-time capable methods for localization and mapping and point
out their strengths and weaknesses. Additionally, we describe algorithms for
state estimation, control and navigation, which use the localization and
mapping results of our vision-based algorithms as input.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065
Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations
Surveying 3D scenes is a common task in robotics. Systems can do so
autonomously by iteratively obtaining measurements. This process of planning
observations to improve the model of a scene is called Next Best View (NBV)
planning.
NBV planning approaches often use either volumetric (e.g., voxel grids) or
surface (e.g., triangulated meshes) representations. Volumetric approaches
generalise well between scenes as they do not depend on surface geometry but do
not scale to high-resolution models of large scenes. Surface representations
can obtain high-resolution models at any scale but often require tuning of
unintuitive parameters or multiple survey stages.
This paper presents a scene-model-free NBV planning approach with a density
representation. The Surface Edge Explorer (SEE) uses the density of current
measurements to detect and explore observed surface boundaries. This approach
is shown experimentally to provide better surface coverage in lower computation
time than the evaluated state-of-the-art volumetric approaches while moving
equivalent distances
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