Semantic labeling of places with mobile robots

نویسنده

  • Óscar Martínez Mozos
چکیده

Indoor environments can typically be divided into places with different function-alities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment, and to improve its capabilites. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a more intuitive way. Other tasks, like exploration or localization, can also be carried out by the robot in a better way when semantic information is taken into account. In this thesis, we present a method that enables a mobile robot to classify the different places of indoor environments into semantic classes, and then use this information to extend its representations of the environments. The main idea is to classify the position of the robot based on the current observations taken by the robot. In this work, we use as main observations the scans obtained from a laser range sensor. Each scan is represented by a set of features that encode the geometrical properties of the current position. These features are then used to classify the scan into the corresponding semantic class. The output of the classification is represented by a probability distribution over the set of possible semantic classes. This probabilistic representation permits us to apply further probabilistic techniques to improve the final classification, reducing the number of errors. We also present an extension which enables the robot to include other types of observations in the classification, like camera images. This work additionally introduces several applications of the previous approach in different robotic tasks. First, we will show how the semantic information can be used to extract topological maps from indoor environments. In a second application , we present a method that incorporates transitions between different places when classifying a trajectory taken by a mobile robot. It will also be shown that the semantic information can reduce the time needed by the robot in exploration and localization tasks. Finally, we present the semantic classification of places as part of an integrated robotic system designed for interacting with humans using natural language. Acknowledgments I would like to thank all the people who made this thesis possible. First of all, I would like to thank Prof. Wolfram Burgard for giving me the opportunity to work in his research group. I must admit that this was one of the …

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Semantic Place Labeling Using a Probabilistic Decision List of AdaBoost Classifiers

The success of mobile robots relies on the ability to extract from the environment additional information beyond simple spatial relations. In particular, mobile robots need to have semantic information about the entities in the environment such as the type or the name of places or objects. This work addresses the problem of classifying places (room, corridor or doorway) using mobile robots equi...

متن کامل

Semantic Place Classification of Indoor Environments with Mobile Robots Using Boosting

Indoor environments can typically be divided into places with different functionalities like kitchens, offices, or seminar rooms. We believe that such semantic information enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, or localization. This paper presents a supervised learning approach to label different locations using b...

متن کامل

Using AdaBoost for Place Labeling and Topological Map Building

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that the ability to learn such semantic categories from sensor data or in maps enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, exploration, or localization. In this work, w...

متن کامل

Supervised semantic labeling of places using information extracted from sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction with humans. As an example, natural language terms like “corridor” or “room” can be used to communicate the pos...

متن کامل

Delay Compensation on Fuzzy Trajectory Tracking Control of Omni-Directional Mobile Robots

This paper presents a delay compensator fuzzy control for trajectory tracking of omni-directional mobile robots. Fuzzy logic control (FLC) of the robots is a suitable strategy for dealing with model uncertainties, nonlinearities and disturbances.  On the other hand, in many robotic applications such as mobile robots, delay phenomenon is able to substantially deteriorate the behavior of system's...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره 61  شماره 

صفحات  -

تاریخ انتشار 2008