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Detekcja obiektów z wykorzystaniem probabilistycznych modeli grafowych

Wietrzykowski J.

Prace Naukowe Politechniki Warszawskiej. Elektronika

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2016

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z. 195, t. 2

313--324

PL

Detekcja obiektów na obrazach RGB jest ważnym zagadnieniem w kontekście postrzegania otoczenia przez autonomiczne roboty. W niniejszej pracy zaproponowano i przetestowano nowe podejście do tego problemu. Podejście to łączy wykorzystanie hierarchii części jako kompaktowej reprezentacji modelu obiektu oraz probabilistycznych modeli grafowych jako metody wnioskowania o obecności obiektów. Hierarchia składa się części, które zostały podzielone na warstwy. Każda część jest kompozycją kilku części z niższej warstwy, wyuczoną na podstawie przykładowych obrazów. Części są współdzielone pomiędzy kategoriami obiektów, co znacznie komplikuje proces wnioskowania, lecz daje wiele korzyści. Zaproponowany system może w przyszłości służyć do wspomagania nawigacji robotów mobilnych, a także z nimi współpracować. Skuteczność zaprezentowanego rozwiązania przetestowano na ogólnie dostępnym zbiorze ETHZ Shape Classes .

EN

Object detection on RGB images is an important problem in a context of an environment perception by mobile robots. In this work I proposed and tested a novel approach to this problem. The approach combines an usage of a parts hierarchy as a compact object model representation with an utilization of probabilistic graphical models to infer about objects existence. The hierarchy is build from parts that are organised as layers. Every part is a composition of several lower layer parts, learned on a basis of sample images presenting objects. Parts are shared among object categories, which significantly complicates inference process, but brings multiple benefits. The proposed system may aid navigation systems of mobile robots in the future and cooperate with them. An efficiency of the solution was tested on a publicly available ETHZ Shape Classes dataset.

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On the Representation of Planes for Efficient Graph-based SLAM with High-level Features

Wietrzykowski J.

Journal of Automation Mobile Robotics and Intelligent Systems

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2016

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Vol. 10, No. 3

3--11

EN

Despite the fact, that dense SLAM systems are extensively developed and are getting popular, feature based ones still have many advantages over them. One of the most important matters in sparse systems are features. The performance and robustness of a system depends strictly on the quality of constraints imposed by feature observations and reliable matching between measurements and features. To improve those two aspects, higher-level features can be used, and planes are a natural choice. We tackle the problem of plugging planes into the g2o optimization framework with two distinct plane representations: one based on a properly stated SE(3) parametrization and one based on a minimal parametrization analogous to quaternions. Proposed solutions were implemented as extensions to the g2o framework and experiments that verify them were conducted using simulation. We provide a comparison of performance under various conditions that emphasized differences.

3

Boosting support vector machines for RGB-D based terrain classification

Wietrzykowski J., Belter D.

Journal of Automation Mobile Robotics and Intelligent Systems

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2014

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Vol. 8, No. 3

28--34

EN

This paper deals with the terrain classification problem for an autonomous mobile robot. The robot is designed to operate in an outdoor environment. The classifier integrates data from RGB camera and 2D laser scanner. The camera provides information about visual appearance of the objects in front of the robot. The laser scanner provides data about distance to the objects and their ability to reflect infrared beam. In this paper we present the method which create terrain segments and classifies them using joint application of Support Vector Machine (SVM) classifier and AdaBoost algorithm. The classifica- tion results of the experimental verification are provided in the paper.