Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Advances in autonomy of mobile robots: perception and navigation

Praca przedstawia zarys obecnego stanu badań w dziedzinie robotyki zmierzających do zwiększenia zakresu autonomii decyzyjnej robotów mobilnych. Na podstawie studiów literaturowych oraz doświadczeń zespołu badawczego Instytutu Automatyki i Inżynierii Informatycznej (IAiII) Politechniki Poznańskiej stawia się tezę, że zwiększenie autonomii robotów mobilnych oraz przełamanie istniejących dotychczas ograniczeń w zakresie ich funkcji percepcyjnych i nawigacyjnych jest warunkiem szerszego wdrożenia tych robotów w praktycznych zastosowaniach. Przedstawiono dyskusję dotyczącą obecnych i przyszłych zastosowań robotów mobilnych oraz wynikających z nich potrzeb w zakresie percepcji otoczenia i nawigacji. Uwagę skupiono na problemach trójwymiarowej i wielosensorycznej percepcji, metodach budowy modelu otoczenia oraz na zagadnieniu nawigacji autonomicznej w skomplikowanych środowiskach. Rozważania zilustrowano wynikami prac badawczych prowadzonych w IAiII.This article provides an outline of the current research efforts in robotics that aim at extending the autonomy of mobile robots. It is argued that on the basis of a literature study and the experience of the author and his co-laborators, that mobile robots require a substantial degree of autonomy to be widely accepted as usefull in practical applications. A paradigm shift is required in perception and navigation methods to take the mobile robots from well-structured laboratories to real environments, which could be unstructured, cluttered and populated by human beigns and other robots. Current and emerging application areas of mobile robots are discussed briefly, and the requirements as to the perception and navigation capabilities are formulated. The discussion is focused on 3D and multi-sensor perception, semantically upgraded environment modells, and navigation algorithms for challenging scenarios. The discussion is illustrated with results of research conducted at the Institute of Control and Information Engineering, Poznań University of Technology

Uncertainty Models of Vision Sensors in Mobile Robot Positioning

This paper discusses how uncertainty models of vision-based positioning sensors can be used to support the planning and optimization of positioning actions for mobile robots. Two sensor types are considered: a global vision with overhead cameras, and an on-board camera observing artificial landmarks. The developed sensor models are applied to optimize robot positioning actions in a distributed system of mobile robots and monitoring sensors, and to plan the sequence of actions for a robot cooperating with the external infrastructure supporting its navigation

On qualitative uncertainty in range measurements from 2D laser scanners

This paper describes a research concerning recognition, classification, and correction of qualitative-nature errors in range measurements from 2D laser scanners. Nowadays, such scanners are commonly used on mobile robots for navigation. The main cause of qualitative uncertainty is the mixed measurements. This effect has been investigated experimentally for two different classes of the laser range sensors, and explained by analyzinganalysing the physical phenomena underlying their operation. A local grid map has been proposed as an intermediate data structure, which enables to remove the erroneous range measurements, being either mixed measurements, or being caused by dynamic objects in the vicinity of the robot. A novel fuzzy-set-based algorithm has been employed to update evidence in the grid. The results of tests show, that this algorithm is superior to the common Bayesian approach, when qualitative errors in range measurements are present

Opis otoczenia na podstawie danych z sensorów laserowych i wizyjnych

In this paper we, discuss methods to increase the discriminative properties of the laser-based geometric landmarks used in simultaneous localisation and mapping by employing monocular vision data. Vertical edges extracted from images help to estimate the length of the line segments, which are only partially observed. Salient visual features, which defy simple geometric interpretation, are handled by the scale invariant feature transform method. These different types of photometric features are aggregated together with the basic 2D line segments extracted from the laser scanner data into the Perceptually Rich Segments.W pracy przedstawiono metody poprawiające rozróżnialność obiektów geometrycznych wyodrębnionych z danych uzyskanych ze skanera laserowego i wykorzystywanych w systemie jednoczesnej samolokalizacji i budowy mapy otoczenia robota. Założono, że robot porusza się w środowisku zbudowanym przez człowieka, w którym dominują pionowe płaszczyzny (ściany). Poprawę rozróżnialności obiektów uzyskano dzięki wykorzystaniu danych z monookularowego systemu wizyjnego robota. Krawędzie pionowe wyodrębnione z obrazów umożliwiają prawidłową estymację długości odcinków 2D odtworzonych uprzednio na podstawie danych ze skanera laserowego. Fotometryczne cechy znaczące wyodrębniane są z obrazów i opisywane za pomocą metody Scale Invariant Feature Transform (SIFT). Uzyskane wektory parametrów osadzane są następnie w "ramach" tworzonych przez odcinki poziome oraz krawędzie pionowe. Powstają w ten sposób obiekty nowego typu - PRS (ang. Perceptually Rich Segment). Zaprezentowano wyniki eksperymentów dotyczących wyodrębniania i dopasowywania do siebie wektorów SIFT oraz wstępne wyniki dotyczące budowy modelu otoczenia z użyciem obiektów PRS

A software architecture and teleoperation system for a semi-autonomous walking robot

W pracy przedstawiono architekturę najwyższej warstwy systemu sterowania i nawigacji sześcionożnego robota kroczącego Messor. Robot ten charakteryzuje się bogatym zestawem sensorów i zwiększonym zakresem autonomii decyzyjnej, co pozwala na samodzielne pokonywanie trudnego terenu. Przedstawiony system sterowania został zaprojektowany z wykorzystaniem paradygmatu wieloagentowego, co czyni go elastycznym i podatnym na ewentualne rozszerzenia. W artykule przedstawiono strukturę systemu oraz wybrane moduły funkcjonalne realizujące zadania nawigacji i wspomagania teleoperatora.In this paper we present a software architecture of the high-level control and navigation system for the semi-autonomous hexapod robot Messor. This architecture integrates a number of sensory-data processing and planning modules into a system that allows the robot to traverse unknown rugged terrain autonomously. The proposed architecture is based on the multi-agent paradigm, which provides flexibility and makes modularization of the software much easier. Selected components of both the navigation and teleoperation system are presented in more details, to illustrate our research and design methodology

A biologically inspired approach to feasible gait learning for a hexapod robot

The objective of this paper is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot

An educational quadruped robot with hybrid leg-wheel locomotion

Artykuł dotyczy projektu oraz wykonania czworonożnego robota kroczącego wyposażonego w hybrydowy, nożnokołowy mechanizm lokomocji. Mechanizm ten czyni robota interesującym obiektem badawczym i dydaktycznym z punktu widzenia sterowania i planowania ruchu. Zaprezentowano budowę części mechanicznej i systemu sterowania robota oraz jego oprogramowanie. Przedstawiono podstawowe tryby ruchu, obejmujące zarówno ruch kroczący (dyskretny), jak i ruch kołowy (ciągły). Możliwości ruchowe robota zilustrowano także krótkimi filmami, udostępnionymi w Internecie.This paper considers the issues of design and implementation of mechanics, control system and software of a quadruped walking robot. The robot has a hybrid leg-wheel locomotion mechanism, which makes it an interesting subject for studies in robot control and motion planning. The design of the robot's hardware is shown in details, followed by a presentation of the implemented motion strategies, which involve both the legged (discrete) and the wheeled (continuous) modes of locomotion. Results are presented also on movie clips, ahich are made available in the Internet

Improving Self-Localization Efficiency in a Small Mobile Robot by Using a Hybrid Field of View Vision System

In this article a self-localization system for small mobile robots based on inexpensive cameras and unobtrusive, passive landmarks is presented and evaluated. The main contribution is the experimental evaluation of the hybrid field of view vision system for self-localization with artificial landmarks. The hybrid vision system consists of an omnidirectional, upward-looking camera with a mirror, and a typical, front-view camera. This configuration is inspired by the peripheral and foveal vision co-operation in animals. We demonstrate that the omnidirectional camera enables the robot to detect quickly landmark candidates and to track the already known landmarks in the environment. The front-view camera guided by the omnidirectional information enables precise measurements of the landmark position over extended distances. The passive landmarks are based on QR codes, which makes possible to easily include in the landmark pattern additional information relevant for navigation. We present evaluation of the positioning accuracy of the system mounted on a SanBot Mk II mobile robot. The experimental results demonstrate that the hybrid field of view vision system and the QR code landmarks enable the small mobile robot to navigate safely along extended paths in a typical home environment

Experimental verification of a walking robot self - localization system with the kinect sensor

In this paper we investigate methods for self-localisation of a walking robot with the Kinect 3D active range sensor. The Iterative Closest Point (ICP) algorithm is considered as the basis for the computation of the robot rotation and translation between two viewpoints. As an alternative, a feature-based method for matching of 3D range data is considered, using the Normal Aligned Radial Feature (NARF) descriptors. Then, it is shown that NARFs can be used to compute a good initial estimate for the ICP algorithm, resulting in convergent estimation of the sensor egomotion. Experimental results are provided