304 research outputs found
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth Uncertainty
This work proposes a robust visual odometry method for structured
environments that combines point features with line and plane segments,
extracted through an RGB-D camera. Noisy depth maps are processed by a
probabilistic depth fusion framework based on Mixtures of Gaussians to denoise
and derive the depth uncertainty, which is then propagated throughout the
visual odometry pipeline. Probabilistic 3D plane and line fitting solutions are
used to model the uncertainties of the feature parameters and pose is estimated
by combining the three types of primitives based on their uncertainties.
Performance evaluation on RGB-D sequences collected in this work and two public
RGB-D datasets: TUM and ICL-NUIM show the benefit of using the proposed depth
fusion framework and combining the three feature-types, particularly in scenes
with low-textured surfaces, dynamic objects and missing depth measurements.Comment: Major update: more results, depth filter released as opensource, 34
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Using Surfaces and Surface Relations in an Early Cognitive Vision System
The final publication is available at Springer via http://dx.doi.org/10.1007/s00138-015-0705-yWe present a deep hierarchical visual system with two parallel hierarchies for edge and surface information. In the two hierarchies, complementary visual information is represented on different levels of granularity together with the associated uncertainties and confidences. At all levels, geometric and appearance information is coded explicitly in 2D and 3D allowing to access this information separately and to link between the different levels. We demonstrate the advantages of such hierarchies in three applications covering grasping, viewpoint independent object representation, and pose estimation.European Community’s Seventh Framework Programme FP7/IC
Globally Consistent Indoor Mapping via a Decoupling Rotation and Translation Algorithm Applied to RGB-D Camera Output
This paper presents a novel RGB-D 3D reconstruction algorithm for the indoor environment. The method can produce globally-consistent 3D maps for potential GIS applications. As the consumer RGB-D camera provides a noisy depth image, the proposed algorithm decouples the rotation and translation for a more robust camera pose estimation, which makes full use of the information, but also prevents inaccuracies caused by noisy depth measurements. The uncertainty in the image depth is not only related to the camera device, but also the environment; hence, a novel uncertainty model for depth measurements was developed using Gaussian mixture applied to multi-windows. The plane features in the indoor environment contain valuable information about the global structure, which can guide the convergence of camera pose solutions, and plane and feature point constraints are incorporated in the proposed optimization framework. The proposed method was validated using publicly-available RGB-D benchmarks and obtained good quality trajectory and 3D models, which are difficult for traditional 3D reconstruction algorithm
Multi-View Object Instance Recognition in an Industrial Context
We present a fast object recognition system coding shape by viewpoint invariant geometric relations and appearance information. In our advanced industrial work-cell, the system can observe the work space of the robot by three pairs of Kinect and stereo cameras allowing for reliable and complete object information. From these sensors, we derive global viewpoint invariant shape features and robust color features making use of color normalization techniques.
We show that in such a set-up, our system can achieve high performance already with a very low number of training samples, which is crucial for user acceptance and that the use of multiple views is crucial for performance. This indicates that our approach can be used in controlled but realistic industrial contexts that require—besides high reliability—fast processing and an intuitive and easy use at the end-user side.European UnionDanish Council for Strategic Researc
Multi-View Object Instance Recognition in an Industrial Context
We present a fast object recognition system coding shape by viewpoint invariant geometric relations and appearance information. In our advanced industrial work-cell, the system can observe the work space of the robot by three pairs of Kinect and stereo cameras allowing for reliable and complete object information. From these sensors, we derive global viewpoint invariant shape features and robust color features making use of color normalization techniques.
We show that in such a set-up, our system can achieve high performance already with a very low number of training samples, which is crucial for user acceptance and that the use of multiple views is crucial for performance. This indicates that our approach can be used in controlled but realistic industrial contexts that require—besides high reliability—fast processing and an intuitive and easy use at the end-user side.European UnionDanish Council for Strategic Researc
Learning object relationships which determine the outcome of actions
Peer reviewedPublisher PD
Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey
Ensuring traffic safety is crucial, which necessitates the detection and
prevention of road surface defects. As a result, there has been a growing
interest in the literature on the subject, leading to the development of
various road surface defect detection methods. The methods for detecting road
defects can be categorised in various ways depending on the input data types or
training methodologies. The predominant approach involves image-based methods,
which analyse pixel intensities and surface textures to identify defects.
Despite their popularity, image-based methods share the distinct limitation of
vulnerability to weather and lighting changes. To address this issue,
researchers have explored the use of additional sensors, such as laser scanners
or LiDARs, providing explicit depth information to enable the detection of
defects in terms of scale and volume. However, the exploration of data beyond
images has not been sufficiently investigated. In this survey paper, we provide
a comprehensive review of road surface defect detection studies, categorising
them based on input data types and methodologies used. Additionally, we review
recently proposed non-image-based methods and discuss several challenges and
open problems associated with these techniques.Comment: Survey paper
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