12,412 research outputs found
Positional estimation techniques for an autonomous mobile robot
Techniques for positional estimation of a mobile robot navigation in an indoor environment are described. A comprehensive review of the various positional estimation techniques studied in the literature is first presented. The techniques are divided into four different types and each of them is discussed briefly. Two different kinds of environments are considered for positional estimation; mountainous natural terrain and an urban, man-made environment with polyhedral buildings. In both cases, the robot is assumed to be equipped with single visual camera that can be panned and tilted and also a 3-D description (world model) of the environment is given. Such a description could be obtained from a stereo pair of aerial images or from the architectural plans of the buildings. Techniques for positional estimation using the camera input and the world model are presented
Semantic labeling of places using information extracted from laser and vision sensor data
Indoor environments can typically be divided into places with different functionalities like corridors, kitchens,
offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly,
we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor
environments
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ďŹndings
in cognitive psychology, our model is composed of layers representing maps at diďŹerent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Online Context-based Object Recognition for Mobile Robots
This work proposes a robotic object recognition
system that takes advantage of the contextual information latent
in human-like environments in an online fashion. To fully leverage
context, it is needed perceptual information from (at least) a
portion of the scene containing the objects of interest, which could
not be entirely covered by just an one-shot sensor observation.
Information from a larger portion of the scenario could still
be considered by progressively registering observations, but this
approach experiences difficulties under some circumstances, e.g.
limited and heavily demanded computational resources, dynamic
environments, etc. Instead of this, the proposed recognition
system relies on an anchoring process for the fast registration
and propagation of objectsâ features and locations beyond the
current sensor frustum. In this way, the system builds a graphbased
world model containing the objects in the scenario (both
in the current and previously perceived shots), which is exploited
by a Probabilistic Graphical Model (PGM) in order to leverage
contextual information during recognition. We also propose a
novel way to include the outcome of local object recognition
methods in the PGM, which results in a decrease in the usually
high CRF learning complexity. A demonstration of our proposal
has been conducted employing a dataset captured by a mobile
robot from restaurant-like settings, showing promising results.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech
Under vehicle perception for high level safety measures using a catadioptric camera system
In recent years, under vehicle surveillance and the classification of the vehicles become an indispensable task that must be achieved for security measures in certain areas such as shopping centers, government buildings, army camps etc. The main challenge to achieve this task is to monitor the under
frames of the means of transportations. In this paper, we present a novel solution to achieve this aim. Our solution consists of three main parts: monitoring, detection and classification. In the first part we design a new catadioptric camera system in which the perspective camera points downwards to the catadioptric mirror mounted to the body of a mobile robot. Thanks to the
catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part we use speeded up robust features (SURF) in an object recognition algorithm. Fast appearance based mapping algorithm (FAB-MAP) is exploited for the classification of the means of transportations in the third
part. Proposed technique is implemented in a laboratory environment
- âŚ