972 research outputs found
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment
Non-iterative RGB-D-inertial Odometry
This paper presents a non-iterative solution to RGB-D-inertial odometry
system. Traditional odometry methods resort to iterative algorithms which are
usually computationally expensive or require well-designed initialization. To
overcome this problem, this paper proposes to combine a non-iterative front-end
(odometry) with an iterative back-end (loop closure) for the RGB-D-inertial
SLAM system. The main contribution lies in the novel non-iterative front-end,
which leverages on inertial fusion and kernel cross-correlators (KCC) to match
point clouds in frequency domain. Dominated by the fast Fourier transform
(FFT), our method is only of complexity , where is
the number of points. Map fusion is conducted by element-wise operations, so
that both time and space complexity are further reduced. Extensive experiments
show that, due to the lightweight of the proposed front-end, the framework is
able to run at a much faster speed yet still with comparable accuracy with the
state-of-the-arts
Visual-Inertial Mapping with Non-Linear Factor Recovery
Cameras and inertial measurement units are complementary sensors for
ego-motion estimation and environment mapping. Their combination makes
visual-inertial odometry (VIO) systems more accurate and robust. For globally
consistent mapping, however, combining visual and inertial information is not
straightforward. To estimate the motion and geometry with a set of images large
baselines are required. Because of that, most systems operate on keyframes that
have large time intervals between each other. Inertial data on the other hand
quickly degrades with the duration of the intervals and after several seconds
of integration, it typically contains only little useful information.
In this paper, we propose to extract relevant information for visual-inertial
mapping from visual-inertial odometry using non-linear factor recovery. We
reconstruct a set of non-linear factors that make an optimal approximation of
the information on the trajectory accumulated by VIO. To obtain a globally
consistent map we combine these factors with loop-closing constraints using
bundle adjustment. The VIO factors make the roll and pitch angles of the global
map observable, and improve the robustness and the accuracy of the mapping. In
experiments on a public benchmark, we demonstrate superior performance of our
method over the state-of-the-art approaches
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
Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization
We present VI-DSO, a novel approach for visual-inertial odometry, which
jointly estimates camera poses and sparse scene geometry by minimizing
photometric and IMU measurement errors in a combined energy functional. The
visual part of the system performs a bundle-adjustment like optimization on a
sparse set of points, but unlike key-point based systems it directly minimizes
a photometric error. This makes it possible for the system to track not only
corners, but any pixels with large enough intensity gradients. IMU information
is accumulated between several frames using measurement preintegration, and is
inserted into the optimization as an additional constraint between keyframes.
We explicitly include scale and gravity direction into our model and jointly
optimize them together with other variables such as poses. As the scale is
often not immediately observable using IMU data this allows us to initialize
our visual-inertial system with an arbitrary scale instead of having to delay
the initialization until everything is observable. We perform partial
marginalization of old variables so that updates can be computed in a
reasonable time. In order to keep the system consistent we propose a novel
strategy which we call "dynamic marginalization". This technique allows us to
use partial marginalization even in cases where the initial scale estimate is
far from the optimum. We evaluate our method on the challenging EuRoC dataset,
showing that VI-DSO outperforms the state of the art
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