Electrical and Computer Engineering, Department of
http://hdl.handle.net/1974/768
2017-06-24T01:56:11ZVirtual Interest Point for Registration
http://hdl.handle.net/1974/15865
Virtual Interest Point for Registration
Ahmed, Mirza
A new method is presented for registration of two partially overlapping noisy point clouds that is robust to noise and data density variation with improved computational efficiency. The registration is driven by establishing correspondences of virtual interest points, which do not exist in the original point cloud data and are defined by the intersection of three implicit surfaces extracted from the point cloud. Implicit surfaces exist in abundance in both natural and built environments and can be used to represent stable regions in the data, which in turn leads to repeatable virtual interest points. Large regions in a point cloud can be represented by a few implicit surfaces, which reduces the computational cost of registration and also makes the algorithm robust to noise and data density variations.
The main contribution of this work is to represent the point cloud as implicit surfaces that results in repeatable interest points. The effect of noise is reduced during the modelling phase. Additionally, the feature descriptors computed for the virtual interest points are significantly different from the state of the art techniques. Surface properties and their relationships with each other are used to define a descriptor that is robust to data density variations compared to conventional support region based descriptors. Furthermore, the transformation between two point clouds can be computed by only one true correspondence, which makes the technique efficient compared to recently proposed competing techniques.
Experiments were performed on 11 data sets to characterize robustness to noise and data density variations as well as computational efficiency. The data sets were extracted from natural scenes, including as plants, rocks, and indoor architectural scenes such as offices and laboratories. Similarly, several 3D models were also tested for registration to demonstrate the generality of the technique. A range of sensors was used to collect the data sets, including the Microsoft Kinect version 1 and version 2, Swiss Ranger, and a NextEngine 3D scanner. For most data sets, the proposed method outperformed the Iterative Closest Point (ICP), Generalized Iterative Closest Point, a 2.5D SIFT-based RANSAC method, Super 4-Point Congruent Sets (4PCS), Super Generalized 4PCS (SG4PCS), and the Go-ICP method in registering overlapping point clouds with both a higher success rate and reduced computational cost. The convergence rate of VIP method was more than 75 % for all data sets. The minimum improvement ratio for computational efficiency was more than 1.5 as compared to 4PCS, SG4PCS and Go-ICP for all data sets except Sailor.
Identification and Rendering of Contact Dynamic Models in Robotic and Haptic Systems
http://hdl.handle.net/1974/15773
Identification and Rendering of Contact Dynamic Models in Robotic and Haptic Systems
Schindeler, Ryan
Haptic simulation systems allow kinesthetic interaction between users and virtual environments. These environments are represented using mathematical models which simulate the contact dynamics of real objects. However, the discrete and quantized nature of conventional haptic systems limits the mechanical impedance of the virtual environment and thus degrades the realism of the simulation. Digital haptic systems are particularly limited in their ability to render highly damped environments. Analog control is proposed as an alternative for implementing high impedance virtual environments, and a study of analog haptic stability is presented. A mixed analog-digital is also proposed which uses nonlinear analog feedback and real-time parameter update algorithms to render complex and multilayer analog environments.
The accuracy of environment models is critical for realistic haptic simulations. Soft environments with limited deformation have been shown to be more accurately described using the nonlinear Hunt-Crossley model than linear models. The thesis also proposed a novel real-time Hunt-Crossley identification algorithm based on a polynomial approximation. Experimental results point at superior accuracy and convergence rate as compared to existing Hunt-Crossley estimation algorithms. The proposed identification method can also be used to facilitate robotic contact tasks.
Bayesian Detection of a Change in a Random Sequence with Unknown Initial and Final Distributions
http://hdl.handle.net/1974/15747
Bayesian Detection of a Change in a Random Sequence with Unknown Initial and Final Distributions
Falt, James
Quickest detection is a class of detection problems whereby the objective is to identify a change in distribution of an observed sequence of random variables as quickly as possible. Quickest detection has been applied to a wide range of applications, such as process monitoring, quality control, and disaster detection. In each of these applications, the initial state of the observed sequence is generally known. Considering these applications, there is an abundance of literature considering formulations of the quickest detection problem where the initial state of the sequence is assumed to be known. However, in some applications, the assumption of knowledge of the initial state of the observed sequence is not valid in general. Recently, spectrum sensing, the process of identifying wireless channel characteristics for the application of cognitive radio, has been cast as a quickest detection problem. Upon, first observation, the radio performing spectrum sensing would not know the initial state of the channel, rendering previous formulations of the quickest detection problem unusable here.
In this thesis, an alternative formulation of the quickest detection problem is considered where the initial state of the observed sequence is assumed to be unknown. The problem is formulated as an optimal stopping problem, and a quickest detection scheme is developed based on Bayesian hypothesis testing and an assumed set of costs. The proposed sequential change detector tracks the minimum-risk hypotheses using a time-recursive algorithm which achieves constant computational complexity. It is shown analytically and via simulations that (i) the probability of detecting a change from an incorrect initial distribution asymptotically vanishes over time under suitable parameter choices, (ii) cost parameter choices trade off the probability of early detection of a change (false alarm) against the average delay to detection of a change, and (iii) cost parameter choices determine the certainty with which the initial distribution of the sequence is identified, trading off the probability of detecting a change from an incorrect initial distribution with the ability to detect early changes.
Multiuser Transmit and Receive Beamforming for One-dimensional Signalling
http://hdl.handle.net/1974/15390
Multiuser Transmit and Receive Beamforming for One-dimensional Signalling
Bavand, Majid
Internet of things (IoT), which enables connectivity of billions of devices to the Internet,
is expected to be the next revolution in wireless ecosystem. Due to lack of available
spectrum, it is imperative for wireless technologies
to reuse spectrum by simultaneously providing service to multiple devices at
the same frequency. Existing wireless technologies are designed to support a
few simultaneous users with high data rates. The IoT, on the other hand, requires
support for many users each having a very low data rate.
The main focus of this dissertation is to support, at physical layer,
massive deployment of low data rate devices
by means of user selection, transmission, and receive techniques.
This dissertation proposes user selection algorithms capable of
selecting twice as many users as the number of transmit antennas in a broadcast
channel by employing the concept of orthogonality in low-pass representation of
signals. Moreover,
we propose several reliable multiuser receive beamforming techniques,
specific to low-cost devices with low data rate one-dimensional signalling by
signal processing based on minimization of error probability.
This approach leads to introduction of a new metric called
signal minus interference to noise ratio (SMINR). Maximization
of this metric results in a low-complexity closed-form solution
for the beamforming weights of each user with reliable performance.
This dissertation also proposes several reliable multiuser transmit
precoding techniques, specific to low data rate one-dimensional signalling,
that can support more users than the number of transmit antennas by
employing minimum-probability-of-error as well as widely-linear based signal processing.
The final contribution of this thesis is employing widely linear precoding
for simultaneously transferring information and power in wireless
broadcast channels.
From a more general perspective, this dissertation addresses scenarios where
bandwidth is scarce compared to the density of available users and proposes signal
processing techniques to enable higher network throughput. In particular, it is shown that
grater throughput may be achieved and the number of users can be increased in a
multiuser communications system by using either widely linear or minimum probability
of error based processing of one-dimensionally modulated signals compared to
linear processing of both one-dimensionally and two-dimensionally modulated signals.