Closed
http://hdl.handle.net/10453/226
20150418T04:06:03Z

On the Asymptotic Connectivity of Random Networks under the Random Connection Model
http://hdl.handle.net/10453/32920
On the Asymptotic Connectivity of Random Networks under the Random Connection Model
Mao, G; Anderson, BDO
Consider a network where all nodes are distributed on a unit square following a Poisson distribution with known density $\rho$ and a pair of nodes separated by an Euclidean distance $x$ are directly connected with probability $g(\frac{x}{r_{\rho}})$, where $g:[0,\infty)\rightarrow[0,1]$ satisfies three conditions: rotational invariance, nonincreasing monotonicity and integral boundedness, $r_{\rho}=\sqrt{\frac{\log\rho+b}{C\rho}}$, $C=\int_{\Re^{2}}g(\Vert \boldsymbol{x}\Vert)d\boldsymbol{x}$ and $b$ is a constant, independent of the event that another pair of nodes are directly connected. In this paper, we analyze the asymptotic distribution of the number of isolated nodes in the above network using the ChenStein technique and the impact of the boundary effect on the number of isolated nodes as $\rho\rightarrow\infty$. On that basis we derive a necessary condition for the above network to be asymptotically almost surely connected. These results form an important link in expanding recent results on the connectivity of the random geometric graphs from the commonly used unit disk model to the more generic and more practical random connection model.
20101228T00:00:00Z

Activity recognition from the interactions between an assistive robotic walker and human users
http://hdl.handle.net/10453/32355
Activity recognition from the interactions between an assistive robotic walker and human users
Patel, M; Miro, JV; Dissanayake, G
Detection of individuals' intention from a sequence of actions is an open and complex problem. In this paper we present a smart walker as mobility aid which can interpret the users' behaviour patterns to recognize their intentions and consequently act as an intelligent assistant. The result of the experiments performed in this paper demonstrates the potential of dynamic bayesian networks (DBN), in relation to their dynamic and unsupervised nature, for realistic humanrobot interaction modelling.
20110101T00:00:00Z

A robust RGBD SLAM algorithm
http://hdl.handle.net/10453/32349
A robust RGBD SLAM algorithm
Hu, G; Huang, S; Zhao, L; Alempijevic, A; Dissanayake, G
Recently RGBD sensors have become very popular in the area of Simultaneous Localisation and Mapping (SLAM). The major advantage of these sensors is that they provide a rich source of 3D information at relatively low cost. Unfortunately, these sensors in their current forms only have a range accuracy of up to 4 metres. Many techniques which perform SLAM using RGBD cameras rely heavily on the depth and are restrained to office type and geometrically structured environments. In this paper, a switching based algorithm is proposed to heuristically choose between RGBBA and RGBDBA based local maps building. Furthermore, a low cost and consistent optimisation approach is used to join these maps. Thus the potential of both RGB and depth image information are exploited to perform robust SLAM in more general indoor cases. Validation of the proposed algorithm is performed by mapping a large scale indoor scene where traditional RGBD mapping techniques are not possible. © 2012 IEEE.
20120101T00:00:00Z

A Convex Optimization Based Approach for Pose SLAM Problems
http://hdl.handle.net/10453/32348
A Convex Optimization Based Approach for Pose SLAM Problems
Liu, M; Huang, S; Dissanayake, G; Wang, H
20120101T00:00:00Z