19,045 research outputs found
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
Color-depth cameras (RGB-D cameras) have become the primary sensors in most
robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and
extrinsic calibration that generally does not meet the accuracy requirements
needed by many robotics applications (e.g., highly accurate 3D environment
reconstruction and mapping, high precision object recognition and localization,
...). In this paper, we propose a human-friendly, reliable and accurate
calibration framework that enables to easily estimate both the intrinsic and
extrinsic parameters of a general color-depth sensor couple. Our approach is
based on a novel two components error model. This model unifies the error
sources of RGB-D pairs based on different technologies, such as
structured-light 3D cameras and time-of-flight cameras. Our method provides
some important advantages compared to other state-of-the-art systems: it is
general (i.e., well suited for different types of sensors), based on an easy
and stable calibration protocol, provides a greater calibration accuracy, and
has been implemented within the ROS robotics framework. We report detailed
experimental validations and performance comparisons to support our statements
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
Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts
This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects
We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e.
translation and rotation, of texture-less rigid objects. The dataset features
thirty industry-relevant objects with no significant texture and no
discriminative color or reflectance properties. The objects exhibit symmetries
and mutual similarities in shape and/or size. Compared to other datasets, a
unique property is that some of the objects are parts of others. The dataset
includes training and test images that were captured with three synchronized
sensors, specifically a structured-light and a time-of-flight RGB-D sensor and
a high-resolution RGB camera. There are approximately 39K training and 10K test
images from each sensor. Additionally, two types of 3D models are provided for
each object, i.e. a manually created CAD model and a semi-automatically
reconstructed one. Training images depict individual objects against a black
background. Test images originate from twenty test scenes having varying
complexity, which increases from simple scenes with several isolated objects to
very challenging ones with multiple instances of several objects and with a
high amount of clutter and occlusion. The images were captured from a
systematically sampled view sphere around the object/scene, and are annotated
with accurate ground truth 6D poses of all modeled objects. Initial evaluation
results indicate that the state of the art in 6D object pose estimation has
ample room for improvement, especially in difficult cases with significant
occlusion. The T-LESS dataset is available online at cmp.felk.cvut.cz/t-less.Comment: WACV 201
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