View source for Monocular Simultaneous Localization And Mapping with Moving Object Tracking using Conditional Independent submaps

Realistic, unstructured environments constitute a treacherous playground for
robotic systems in charge of mapping a scene whilst simultaneously localizing
in it. Limited computational resources and low-cost sensing devices make
these tasks more involved.
Whilst state of the art algorithms rely upon the highly restrictive static
environment posit, and might experience consistency issues when estimations
are conﬁned to a single map, this thesis introduces a novel solution that
extends covariance form submapping techniques to dynamic environments,
identifying and tracking moving objects by means of a single camera, and
thus preventing them from corrupting estimations. This result has been
achieved by coupling a pure monocular EKF-SLAM algorithm, working on
conditional independent submaps expressed in the inverse scaling param-
etrization, with a Bearing-Only Tracker. The most recent proposals from
the SLAM research community are thus coalesced in a single system, which
aims to provide on-line, accurate, and reliable navigation in a real, unstructured terrain. Albeit traditional submapping solutions impose statistical
independence among estimates in different maps, the conditional independent
SLAM algorithm adopted allows to exchange information between neighboring
submaps, improving past estimations with current measurements by means of
a back-propagation mechanism. The inverse scaling parametrization presents
a measurement equation which is provably more linear than the commonly
adopted inverse depth parametrization, while at the same time demanding
fewer computational resources (since the number of required parameters for
each element decreases for six to four). Geometrical reasoning has been
exploited to detect dynamical objects in the scene by properly casting maps'
elements in the Uncertain Geometry setting. Finally, an adaptive threshold
over the number of features in each submap has been established by means
of a fuzzy controller, which takes into account maps' fill-in to avoid wasting
computational resources.
A full Matlab implementation of the developed ideas is presented, validat-
ing theoretical results by means of extensive experiments in both simulated
and real environments.

== '''Part 1: project profile''' ==

=== Project name ===
Monocular Simultaneous Localization And Mapping with Moving Object Tracking using Conditional Independent submaps

=== Dates ===
Start date: 2008/08/01

End date: 2009/04/20

=== People involved ===

===== Project head(s) =====
* [[User:MatteoMatteucci|Matteo Matteucci]]

===== Other Politecnico di Milano people =====
* [[User:DavideMigliore|Davide Migliore]]

===== Students currently working on the project =====
* [[User:RobertoRigamonti|Roberto Rigamonti]]

===== External personnel: =====
* [[User:DanieleMarzorati|Daniele Marzorati]]
* [[User:Domenicogsorrenti|Domenico G. Sorrenti]]

=== Laboratory work and risk analysis ===
This project does not include laboratory activities.