3,351 research outputs found
UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers
This paper proposes a novel approach for Simultaneous Localization and
Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches
use natural landmarks (such as keypoints). However, they are unstable over
time, repetitive in many cases or insufficient for a robust tracking (e.g. in
indoor buildings). On the other hand, other approaches have employed artificial
landmarks (such as squared fiducial markers) placed in the environment to help
tracking and relocalization. We propose a method that integrates both
approaches in order to achieve long-term robust tracking in many scenarios.
Our method has been compared to the start-of-the-art methods ORB-SLAM2 and
LDSO in the public dataset Kitti, Euroc-MAV, TUM and SPM, obtaining better
precision, robustness and speed. Our tests also show that the combination of
markers and keypoints achieves better accuracy than each one of them
independently.Comment: Paper submitted to Pattern Recognitio
3D human pose estimation from depth maps using a deep combination of poses
Many real-world applications require the estimation of human body joints for
higher-level tasks as, for example, human behaviour understanding. In recent
years, depth sensors have become a popular approach to obtain three-dimensional
information. The depth maps generated by these sensors provide information that
can be employed to disambiguate the poses observed in two-dimensional images.
This work addresses the problem of 3D human pose estimation from depth maps
employing a Deep Learning approach. We propose a model, named Deep Depth Pose
(DDP), which receives a depth map containing a person and a set of predefined
3D prototype poses and returns the 3D position of the body joints of the
person. In particular, DDP is defined as a ConvNet that computes the specific
weights needed to linearly combine the prototypes for the given input. We have
thoroughly evaluated DDP on the challenging 'ITOP' and 'UBC3V' datasets, which
respectively depict realistic and synthetic samples, defining a new
state-of-the-art on them.Comment: Accepted for publication at "Journal of Visual Communication and
Image Representation
Blow-up analysis of conformal metrics of the disk with prescribed Gaussian and geodesic curvatures
This paper is concerned with the compactness of metrics of the disk with
prescribed Gaussian and geodesic curvatures. We consider a blowing-up sequence
of metrics and give a precise description of its asymptotic behavior. In
particular, the metrics blow-up at a unique point on the boundary and we are
able to give necessary conditions on its location. It turns out that such
conditions depend locally on the Gaussian curvatures but they depend on the
geodesic curvatures in a nonlocal way. This is a novelty with respect to the
classical Nirenberg problem where the blow-up conditions are local, and this
new aspect is driven by the boundary condition.Comment: 31 pages, 1 figur
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