Tracking Agents and Predicting Future Agent Motions via Distributed Multi-Clustered Particle Filtering

134 Pages·2016·36.08 MB·English

Tracking Agents and Predicting Future Agent Motions
via Distributed Multi-Clustered Particle Filtering
Department of Computer, Control and Management Engineering
“Antonio Ruberti”, Sapienza University of Rome
Dottorato di Ricerca in Ingegneria Informatica – XXVIII Ciclo
Candidate
Fabio Previtali
ID number 1098909
Thesis Advisor Co-Advisors
Prof. Luca Iocchi Prof. Subramanian Ramamoorthy
Dr. Vincenzo Bonifaci
A thesis submitted in partial fulfilment of the requirements
for the degree of Doctor of Philosophy in Computer Engineering
March 2016
Thesis defended on June, 27th 2016.
External Reviewers:
Prof. Brian D. Ziebart, University of Illinois at Chicago
Prof. Ana C. Murillo, University of Zaragoza
Prof. Pedro U. Lima, University of Lisbon
Tracking Agents and Predicting Future Agent Motions via Distributed Multi-
Clustered Particle Filtering
Ph.D Thesis. Sapienza University of Rome
© 2016 Fabio Previtali. All rights reserved.
This thesis has been typeset by LATEX and the Sapthesis class.
Author’s email: [email protected]
To Federica, my everlasting and unique love
To my (extended) family, which always supported me

v
Acknowledgements
I would like to thank Prof. Daniele Nardi which granted me the opportunity to join
the Lab. RoCoCo research group in September, 2009 and Prof. Luca Iocchi which
guided me during these three years. Both allowed me to reach this big achievement.
My deepest gratitude goes to Prof. Subramanian Ramamoorthy which enlight-
ened me during - and not only - my abroad period at the University of Edinburgh.
Ourchatstrulypositivelycontributedtoimproveandtokeepgoingonmyresearch.
A special thank goes to Dr. Domenico Daniele Bloisi which always pushed me
to go further with his critiques and suggestions.
I would like to thank my friends/collaborators Guglielmo Gemignani, Alejandro
Bordallo Micó, Andrea Pennisi, Francesco Riccio and Roberto Capobianco. Each of
you provide an immense contribution to achieve my objectives.
My sincere gratitude goes to my parents Sergio and Patrizia which always sup-
portedmeduring-andnotonly-mystudies,tomybrotherDaniele whichhasgiven
methefirstprogramminglecturesinPascallayingthefoundationstoneofthismag-
nificent path, to my uncle Rolando, to my aunt Siliana and to my cousins Barbara
and Jessica which each of you contribute to go over any encountered difficulty.
Last but not at all least, an immense and deep thank to Federica my everlasting
and unique love. This achievement has been possible also because of you. I love
you and I always will.

vii
Abstract
This thesis investigates two important problems for intelligent robotic interaction
with other agents: (1) object tracking from multiple - and potentially heteroge-
neous - distributed sensors and (2) predicting future agent motions for interactive
robotic navigation. These problems are motivated by the deficiencies of existing
mobile robots to navigate amongst humans (or other agents) in an intelligent man-
ner similar to how humans are able to co-navigate: by recognising other agents in
the environment, inferring their intentions and planning complementary movement
trajectories that lead to efficient joint optimisation for all agents. Many existing
mobile robots do not reason about the goal-directed movements of others in the
environment, leading to substantial sub-optimality in reaching target locations.
In order to address the first problem, we develop PTracking, an algorithm
for tracking multiple objects from multiple sensors in a distributed manner using
Bayesian filtering (and particle filtering specifically to approximate the generally
intractable inference task). The main novelty of the proposed approach is the com-
bination of clustering and mixture models to enable more computationally efficient
asynchronous inference. We demonstrate the algorithm’s versatility in a number
of realistic applications: robotic soccer, multiple object tracking with mobile sen-
sors, multi-robot surveillance, networked camera tracking of people and maritime
surveillance.
The second problem has been tackled by employing an Inverse Reinforcement
Learning (IRL) approach in combination with PTracking to estimate the reward
functions that motivate observed behaviour sequences. A key innovation is that
unlike previous IRL methods, which typically assume a fixed state-space repre-
sentation, the state-space representation is dynamically adapted in the proposed
method, so that more modelling emphasis is placed on portions of the space that
are frequently visited and less emphasis can be placed on rarely visited portions.
Thisallowssignificantcomputationalsavingsversusemployingauniformlydetailed
state-space representation. We show the benefits of the method for activity fore-
casting applications, intention prediction and for constructing interactive costmaps
to guide robot navigation.

ix
Contents
Acknowledgements v
Abstract vii
1 Introduction 1
1.1 Related Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Related Work 7
2.1 Multiple Object Tracking . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Interactive Motion Planning . . . . . . . . . . . . . . . . . . . . . . . 11
2.3 Activity Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3 Fundamentals 15
3.1 Bayesian Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Kalman Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.3 Particle Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4 Inverse Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . 21
3.5 Robot Operating System . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.6 Microsoft Kinect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Distributed Multi-Clustered Particle Filtering 25
4.1 Sensor Pose Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.2 PTracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.3 Computability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5 Distributed Multi-Agent Multiple Object Tracking 37
5.1 Disambiguating Localisation Symmetry in RoboCup . . . . . . . . . 39
5.1.1 Field Symmetry Disambiguator . . . . . . . . . . . . . . . . . 40
5.1.2 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 42
5.2 Multi-Robots for Prey-Predator Game . . . . . . . . . . . . . . . . . 47
5.2.1 Problem Modelling . . . . . . . . . . . . . . . . . . . . . . . . 48
x Contents
5.2.2 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 48
5.3 Distributed Sensor Network for Multi-Robot Surveillance . . . . . . 53
5.3.1 Problem Modelling . . . . . . . . . . . . . . . . . . . . . . . . 53
5.3.2 Multi-Robot Surveillance System . . . . . . . . . . . . . . . . 54
5.3.3 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 58
5.4 Distributed Camera Network for People Tracking . . . . . . . . . . . 60
5.4.1 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 60
5.5 Enhancing Automatic Maritime Surveillance with Visual Information 65
5.5.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.5.2 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 71
6 Predicting Future Agent Motions 77
6.1 Activity Forecasting via Inverse Reinforcement Learning . . . . . . . 78
6.1.1 Activity Forecasting From Noisy Visual Observations. . . . . 79
6.1.2 Inverse Reinforcement Learning Model . . . . . . . . . . . . . 81
6.1.3 Activity Forecasting and Anomaly Detection . . . . . . . . . 82
6.1.4 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 83
6.2 Intention-PredictionviaCounterfacualReasoningforRobotNavigation 86
6.2.1 Modelling Approach . . . . . . . . . . . . . . . . . . . . . . . 88
6.2.2 Goal Inference . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6.2.3 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 91
6.2.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.3 Interactive Costmaps for Robot Navigation . . . . . . . . . . . . . . 97
6.3.1 Modelling Approach . . . . . . . . . . . . . . . . . . . . . . . 98
6.3.2 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 103
6.3.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
7 Conclusions 111
7.1 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Bibliography 113