33,181 research outputs found
A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots
In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is considered as a minimally invasive novel diagnostic
technology to inspect the entire GI tract and to diagnose various diseases and
pathologies. Since the development of this technology, medical device companies
and many groups have made significant progress to turn such passive capsule
endoscopes into robotic active capsule endoscopes to achieve almost all
functions of current active flexible endoscopes. However, the use of robotic
capsule endoscopy still has some challenges. One such challenge is the precise
localization of such active devices in 3D world, which is essential for a
precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of
the explored inner organ could assist the doctors to make more intuitive and
correct diagnosis. In this paper, we propose to our knowledge for the first
time in literature a visual simultaneous localization and mapping (SLAM) method
specifically developed for endoscopic capsule robots. The proposed RGB-Depth
SLAM method is capable of capturing comprehensive dense globally consistent
surfel-based maps of the inner organs explored by an endoscopic capsule robot
in real time. This is achieved by using dense frame-to-model camera tracking
and windowed surfelbased fusion coupled with frequent model refinement through
non-rigid surface deformations
Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras
Visual scene understanding is an important capability that enables robots to
purposefully act in their environment. In this paper, we propose a novel
approach to object-class segmentation from multiple RGB-D views using deep
learning. We train a deep neural network to predict object-class semantics that
is consistent from several view points in a semi-supervised way. At test time,
the semantics predictions of our network can be fused more consistently in
semantic keyframe maps than predictions of a network trained on individual
views. We base our network architecture on a recent single-view deep learning
approach to RGB and depth fusion for semantic object-class segmentation and
enhance it with multi-scale loss minimization. We obtain the camera trajectory
using RGB-D SLAM and warp the predictions of RGB-D images into ground-truth
annotated frames in order to enforce multi-view consistency during training. At
test time, predictions from multiple views are fused into keyframes. We propose
and analyze several methods for enforcing multi-view consistency during
training and testing. We evaluate the benefit of multi-view consistency
training and demonstrate that pooling of deep features and fusion over multiple
views outperforms single-view baselines on the NYUDv2 benchmark for semantic
segmentation. Our end-to-end trained network achieves state-of-the-art
performance on the NYUDv2 dataset in single-view segmentation as well as
multi-view semantic fusion.Comment: the 2017 IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS 2017
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
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