Abstract:

This paper addresses object perception applied to mobile robotics. Being able to perceive semantically meaningful objects in unstructured environments is a ke capability in order to make robots suitable to perform high-level tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and
proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings.

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Abstract:

This paper presents a vision-based approach
for mobile robot localization. The model of the environment
is topological. The new approach characterize a
place using a signature. This signature consists of a constellation
of descriptors computed over dierent types
of local affine covariant regions extracted from an omnidirectional
image acquired rotating a standard camera
with a pan-tilt unit. This type of representation permits
a reliable and distinctive environment modeling.
Our objectives were to validate the proposed method
in indoor environments and, also, to nd out if the
combination of complementary local feature region detectors
improves the localization versus using a single
region detector. Our experimental results show that if
false matches are efectively rejected, the combination
of dierent covariant affine region detectors increases
notably the performance of the approach by combining
the dierent strengths of the individual detectors.
In order to reduce the localization time, two strategies
are evaluated

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Keywords:

Abstract:

An autonomous mobile robot must face the correspondence
or data association problem in order to carry out
tasks like place recognition or unknown environment mapping. In
order to put into correspondence two maps, most correspondence
methods first extract early features from robot sensor data,
then matches between features are searched and finally the
transformation that relates the maps is estimated from such
matches. However, finding explicit matches between features is a
challenging and computationally expensive task. In this paper, we
propose a new method to align obstacle maps without searching
explicit matches between features. The maps are obtained from a
stereo pair. Then, we use a vocabulary tree approach to identify
putative corresponding maps followed by a Newton minimization
algorithm to find the transformation that relates both maps. The
proposed method is evaluated on a typical office dataset showing
good performance.

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Abstract:

General object recognition in mobile robots is of primary importance
in order to enhance the representation of the environment that robots will use for
their reasoning processes. Therefore, we contribute reduce this gap by evaluating
the SIFT Object Recognition method in a challenging dataset, focusing on issues
relevant to mobile robotics. Resistance of the method to the robotics working conditions
was found, but it was limited mainly to well-textured objects.

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Abstract:

Designing a robot’s behavior in imprecise, uncertain, dynamic, unpredictable and real-time response domains is very challenging, and even more if an adversarial component is also present. An example of such domains is the robot soccer. In this work we propose the use of Case-Based Reasoning techniques to handle all these features in the action selection problem of a team of robots. Moreover, we are also interested in obtaining a cooperative behavior among robots to successfully perform joint tasks. Thus, we include explicit passes between robots, not only to enhance collaboration, but also to face the adversarial component of the domain, i.e. the opponents. We evaluate our approach with respect to a reactive approach in 2 vs. 2 scenarios, where two attackers play against two moving opponents, i.e. a defender and a goalie. We successfully show that our approach not only achieves the expected team behavior, but also outperforms in general the compared approach.

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