5,753 research outputs found
Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect
Recently, the new Kinect One has been issued by Microsoft, providing the next
generation of real-time range sensing devices based on the Time-of-Flight (ToF)
principle. As the first Kinect version was using a structured light approach,
one would expect various differences in the characteristics of the range data
delivered by both devices. This paper presents a detailed and in-depth
comparison between both devices. In order to conduct the comparison, we propose
a framework of seven different experimental setups, which is a generic basis
for evaluating range cameras such as Kinect. The experiments have been designed
with the goal to capture individual effects of the Kinect devices as isolatedly
as possible and in a way, that they can also be adopted, in order to apply them
to any other range sensing device. The overall goal of this paper is to provide
a solid insight into the pros and cons of either device. Thus, scientists that
are interested in using Kinect range sensing cameras in their specific
application scenario can directly assess the expected, specific benefits and
potential problem of either device.Comment: 58 pages, 23 figures. Accepted for publication in Computer Vision and
Image Understanding (CVIU
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
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