8,283 research outputs found
LocNet: Global localization in 3D point clouds for mobile vehicles
Global localization in 3D point clouds is a challenging problem of estimating
the pose of vehicles without any prior knowledge. In this paper, a solution to
this problem is presented by achieving place recognition and metric pose
estimation in the global prior map. Specifically, we present a semi-handcrafted
representation learning method for LiDAR point clouds using siamese LocNets,
which states the place recognition problem to a similarity modeling problem.
With the final learned representations by LocNet, a global localization
framework with range-only observations is proposed. To demonstrate the
performance and effectiveness of our global localization system, KITTI dataset
is employed for comparison with other algorithms, and also on our long-time
multi-session datasets for evaluation. The result shows that our system can
achieve high accuracy.Comment: 6 pages, IV 2018 accepte
Automatic Alignment of 3D Multi-Sensor Point Clouds
Automatic 3D point cloud alignment is a major research topic in photogrammetry, computer vision and computer graphics. In this research, two keypoint feature matching approaches have been developed and proposed for the automatic alignment of 3D point clouds, which have been acquired from different sensor platforms and are in different 3D conformal coordinate systems.
The first proposed approach is based on 3D keypoint feature matching. First, surface curvature information is utilized for scale-invariant 3D keypoint extraction. Adaptive non-maxima suppression (ANMS) is then applied to retain the most distinct and well-distributed set of keypoints. Afterwards, every keypoint is characterized by a scale, rotation and translation invariant 3D surface descriptor, called the radial geodesic distance-slope histogram. Similar keypoints descriptors on the source and target datasets are then matched using bipartite graph matching, followed by a modified-RANSAC for outlier removal.
The second proposed method is based on 2D keypoint matching performed on height map images of the 3D point clouds. Height map images are generated by projecting the 3D point clouds onto a planimetric plane. Afterwards, a multi-scale wavelet 2D keypoint detector with ANMS is proposed to extract keypoints on the height maps. Then, a scale, rotation and translation-invariant 2D descriptor referred to as the Gabor, Log-Polar-Rapid Transform descriptor is computed for all keypoints. Finally, source and target height map keypoint correspondences are determined using a bi-directional nearest neighbour matching, together with the modified-RANSAC for outlier removal.
Each method is assessed on multi-sensor, urban and non-urban 3D point cloud datasets. Results show that unlike the 3D-based method, the height map-based approach is able to align source and target datasets with differences in point density, point distribution and missing point data. Findings also show that the 3D-based method obtained lower transformation errors and a greater number of correspondences when the source and target have similar point characteristics. The 3D-based approach attained absolute mean alignment differences in the range of 0.23m to 2.81m, whereas the height map approach had a range from 0.17m to 1.21m. These differences meet the proximity requirements of the data characteristics and the further application of fine co-registration approaches
Configurable Input Devices for 3D Interaction using Optical Tracking
Three-dimensional interaction with virtual objects is one of the aspects that needs to be addressed
in order to increase the usability and usefulness of virtual reality. Human beings
have difficulties understanding 3D spatial relationships and manipulating 3D user interfaces,
which require the control of multiple degrees of freedom simultaneously. Conventional interaction
paradigms known from the desktop computer, such as the use of interaction devices as
the mouse and keyboard, may be insufficient or even inappropriate for 3D spatial interaction
tasks.
The aim of the research in this thesis is to develop the technology required to improve 3D
user interaction. This can be accomplished by allowing interaction devices to be constructed
such that their use is apparent from their structure, and by enabling efficient development of
new input devices for 3D interaction.
The driving vision in this thesis is that for effective and natural direct 3D interaction the
structure of an interaction device should be specifically tuned to the interaction task. Two
aspects play an important role in this vision. First, interaction devices should be structured
such that interaction techniques are as direct and transparent as possible. Interaction techniques
define the mapping between interaction task parameters and the degrees of freedom of
interaction devices. Second, the underlying technology should enable developers to rapidly
construct and evaluate new interaction devices.
The thesis is organized as follows. In Chapter 2, a review of the optical tracking field is
given. The tracking pipeline is discussed, existing methods are reviewed, and improvement
opportunities are identified.
In Chapters 3 and 4 the focus is on the development of optical tracking techniques of rigid
objects. The goal of the tracking method presented in Chapter 3 is to reduce the occlusion
problem. The method exploits projection invariant properties of line pencil markers, and the
fact that line features only need to be partially visible.
In Chapter 4, the aim is to develop a tracking system that supports devices of arbitrary
shapes, and allows for rapid development of new interaction devices. The method is based on
subgraph isomorphism to identify point clouds. To support the development of new devices
in the virtual environment an automatic model estimation method is used.
Chapter 5 provides an analysis of three optical tracking systems based on different principles.
The first system is based on an optimization procedure that matches the 3D device
model points to the 2D data points that are detected in the camera images. The other systems
are the tracking methods as discussed in Chapters 3 and 4.
In Chapter 6 an analysis of various filtering and prediction methods is given. These
techniques can be used to make the tracking system more robust against noise, and to reduce
the latency problem.
Chapter 7 focusses on optical tracking of composite input devices, i.e., input devices
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that consist of multiple rigid parts that can have combinations of rotational and translational
degrees of freedom with respect to each other. Techniques are developed to automatically
generate a 3D model of a segmented input device from motion data, and to use this model to
track the device.
In Chapter 8, the presented techniques are combined to create a configurable input device,
which supports direct and natural co-located interaction. In this chapter, the goal of the thesis
is realized. The device can be configured such that its structure reflects the parameters of the
interaction task.
In Chapter 9, the configurable interaction device is used to study the influence of spatial
device structure with respect to the interaction task at hand. The driving vision of this thesis,
that the spatial structure of an interaction device should match that of the task, is analyzed
and evaluated by performing a user study.
The concepts and techniques developed in this thesis allow researchers to rapidly construct
and apply new interaction devices for 3D interaction in virtual environments. Devices
can be constructed such that their spatial structure reflects the 3D parameters of the interaction
task at hand. The interaction technique then becomes a transparent one-to-one mapping
that directly mediates the functions of the device to the task. The developed configurable interaction
devices can be used to construct intuitive spatial interfaces, and allow researchers to
rapidly evaluate new device configurations and to efficiently perform studies on the relation
between the spatial structure of devices and the interaction task
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