Random Finite Sets for Robot Map** and SLAM
New Concepts in Autonomous Robotic Map Representations
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Chapter and Conference Paper
A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences
Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking...
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Book
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Chapter and Conference Paper
Visual Tracking of Multiple Targets by Multi-Bernoulli Filtering of Background Subtracted Image Data
Most visual multi-target tracking techniques in the literature employ a detection routine to map the image data to point measurements that are usually further processed by a filter. In this paper, we present a...
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Chapter
Introduction
Machines which perceive the world through the use of sensors, make computational decisions based on the sensors’ outputs and then influence the world with actuators, are broadly labelled as “Robots”. Due to th...
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Chapter
Estimation with Random Finite Sets
The previous chapter provided the motivation to adopt an RFS representation for the map in both FBRM and SLAM problems. The main advantage of the RFS formulation is that the dimensions of the measurement likel...
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Chapter
An RFS ‘Brute Force’ Formulation for Bayesian SLAM
The feature-based (FB) SLAM scenario is a vehicle moving through an environment represented by an unknown number of features. The classical problem definition is one of “a state estimation problem involving a var...
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Chapter
Extensions with RFSs in SLAM
This book demonstrates that the inherent uncertainty of feature maps and feature map measurements can be naturally encapsulated by random finite set models, and subsequently in Chapter 5 proposed the multi-fea...
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Chapter and Conference Paper
Mobile Robotics in a Random Finite Set Framework
This paper describes the Random Finite Set approach to Bayesian mobile robotics, which is based on a natural multi-object filtering framework, making it well suited to both single and swarm-based mobile roboti...
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Chapter
Why Random Finite Sets?
We begin the justification for the use of RFSs by re-evaluating the basic issues of feature representation, and considering the fundamental mathematical relationship between environmental feature representatio...
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Chapter
An RFS Theoretic for Bayesian Feature-Based Robotic Map**
Estimating a FB map requires the joint propagation of the FB map density encapsulating uncertainty in feature number and location. This chapter addresses the joint propagation of the FB map density and leads t...
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Chapter
Rao-Blackwellised RFS Bayesian SLAM
This chapter proposes an alternative Bayesian framework for feature-based SLAM, again in the general case of uncertain feature number and data association. As in Chapter 5, a first order solution, coined the p...
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Article
Convergence of the SMC Implementation of the PHD Filte
The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-obje...
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Book
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Chapter
Introduction
In signal processing, the design of many filters can often be cast as a constrained optimization problem where the constraints are defined by the specifications of the filter. These specifications can arise ei...
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Chapter
Filtering with Convex Response Constraints
The envelope constrained (EC) filtering problem has been outlined in Chapter 1 as a constrained optimization problem in Hilbert space, where the filter’s response to a prescribed signal is required to stay ins...
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Chapter
Discrete-Time EC Filtering Algorithms
With the mathematical frame work established, we can now focus on the construction and characterization of algorithms for computing solutions. Convex programming is a broad class of problems and there is no ge...
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Chapter
Robust Envelope Constrained Filtering
In Chapters 4 and 5, we studied numerical methods for finding a filter whose response to a specified signal fits into a given envelope. Assuming that the set of feasible filters does not contain the origin, i....
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Chapter
Analysis and Problem Characterization
The previous chapter addresses the envelope constrained (EC) filtering problem from the general view point of a convex programming problem by examining properties of the cost functional and the feasible region...
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Chapter
Numerical Methods for Continuous-Time EC Filtering
In Chapter 2, the continuous-time EC filtering problem has been formulated for both a purely analog filter structure and a hybrid filter structure that includes analog and digital signal processing components....
