Indoor Localization System based on Artificial Landmarks and Monocular Vision

 This paper presents a visual localization approach that is suitable for domestic and industrial environments as it enables accurate, reliable and robust pose estimation. The mobile robot is equipped with a single camera which update sits pose whenever a landmark is available on the field of view. The innovation presented by this research focuses on the artificial landmark system which has the ability to detect the presence of the robot, since both entities communicate with each other using an infrared signal protocol modulated in frequency. Besides this communication capability, each landmark has several high intensity light-emitting diodes (LEDs) that shine only for some instances according to the communication, which makes it possible for the camera shutter and the blinking of the LEDs to synchronize. This synchronization increases the system tolerance concerning changes in brightness in the ambient lights over time, independently of the landmarks location. Therefore, the environment’s ceiling is populated with several landmarks and an Extended Kalman Filter is used to combine the dead-reckoning and landmark information. This increases the flexibility of the system by reducing the number of landmarks required. The experimental evaluation was conducted in a real indoor environment with an autonomous wheelchair prototype

Robust mobile robot localization based on security laser scanner

This paper addresses the development of a new localization system based on a security laser presented on most AGVs for safety reasons. An enhanced artificial beacons detection algorithm is applied with a combination of a Kalman filter and an outliers rejection method in order to increase the robustness and precision of the system. This new robust approach allows to implement such system in current AGVs. Real results in industrial environment validate the proposed methodology.The work presented in this paper, being part of the Project "NORTE-07-0124-FEDER-000060" is financed by the North Portugal Regional Operational Programme (ON.2 – O Novo Norte), under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

Robust mobile robot localization based on a security laser: An industry case study

This paper aims to address a mobile robot localization system that avoids using a dedicated laser scanner, making it possible to reduce implementation costs and the robot's size. The system has enough precision and robustness to meet the requirements of industrial environments. Design/methodology/approach - Using an algorithm for artificial beacon detection combined with a Kalman Filter and an outlier rejection method, it was possible to enhance the precision and robustness of the overall localization system. Findings - Usually, industrial automatic guide vehicles feature two kinds of lasers: one for navigation placed on top of the robot and another for obstacle detection (security lasers). Recently, security lasers extended their output data with obstacle distance (contours) and reflectivity. These new features made it possible to develop a novel localization system based on a security laser. Research limitations/implications - Once the proposed methodology is completely validated, in the future, a scheme for global localization and failure detection should be addressed. Practical implications - This paper presents a comparison between the presented approach and a commercial localization system for industry. The proposed algorithms were tested in an industrial application under realistic working conditions. Social implications - The presented methodology represents a gain in the effective cost of the mobile robot platform, as it discards the need for a dedicated laser for localization purposes. Originality/value - This paper presents a novel approach that benefits from the presence of a security laser on mobile robots (mandatory sensor when considering industrial applications), using it simultaneously with other sensors, not only to guarantee safety conditions during operation but also to locate the robot in the environment. This paper is also valuable because of the comparison made with a commercialized system, as well as the tests conducted in real industrial environments, which prove that the approach presented is suitable for working under these demanding conditions.Project "TEC4Growth" - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020" is fnanced by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Probabilistic Localization of a Soccer Robot

Mobiilsed autonoomsed robotid vajavad iseseisvaks navigeerimiseks teadmist oma umbkaudse asukoha kohta. Tihtipeale pole see otseselt tuvastatav, vaid roboti positsioon tuleb järeldada mitmete müraste sensorite mõõtmistest. Antud tees tegeleb probleemiga, kuidas lokaliseerida iseseisvat jalgpallirobotit videopildi alusel. Kasutatakse statistilisi Bayesi filtreerimise meetodeid nagu Kalmani- ja osakeste filter, mis arvestavad sellistele süsteemidele omase müra ja ebakindlusega. Implementeeritakse ja võrreldakse mitmeid erinevaid lokalisatsioonialgoritme ja testitakse neid ka lisaks simulaatorile ka füüsilise roboti peal. Töötatakse välja toimiv praktiline lahendus mobiilse jalgpalliroboti lokaliseerimiseks.The thesis deals with the problem of localizing a mobile soccer-playing robot using Bayes filtering methods. For navigating natural environments, autonomous robots need to know where they are located even if the position of the robot is not directly observable, but rather needs to be inferred from indirect measurements of several noisy sensors. The algorithms need to account for the inherent uncertainty of such systems. Several algorithms of robot positioning including Kalman filter and particle filter are investigated, implemented and compared. The algorithms are also tested on a real robot. A working solution for practical robot localization is developed

New approach for beacons based mobile robot localization using kalman filters

New approaches on industrial mobile robots are changing the localization systems from old methods such as magnetic tapes to laser beacons based systems and natural landmarks since they are more adaptable and easier to install on the shop floor. Sensor fusion methods needs to be applied since there is information provided from different sources. Extended Kalman Filters are very used in the pose estimation of mobile robots with sensors that detect beacons and measure its distance and angle in a local referential frame. In certain situations, like for example wheels slippage, the number of impulses read for the encoders is wrong, resulting in a very large displacement or rotation and causing a bad estimation at the end of the prediction step. This bad estimation is used for the linearization of the non-linear equations, causing a bad linear approximation and probably a failure in the Kalman Filter. In this paper it is demonstrated that if we use the last state estimation calculated in the update step at the last cycle, instead of the estimation from the prediction step in the actual cycle, the result is an estimator much more robust to errors in the odometry information. Simulated and real results from several experiments are illustrated to demonstrate this new approach.This work is co-financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 and the Lisboa2020 under the PORTUGAL 2020 Partnership Agreement, and through the Portuguese National Innovation Agency (ANI) as a part of project PRODUTECH SIF: POCI01-0247-FEDER-024541. This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme, and by National Funds through the Portuguese funding agency, FCT - Fundac¸ao para a Ci ˜ encia e a Tecnolo- ˆ gia, within project SAICTPAC/0034/2015- POCI-01-0145- FEDER-016418.info:eu-repo/semantics/publishedVersio

Multi-Robot FastSLAM for Large Domains

For a robot to build a map of its surrounding area, it must have accurate position information within the area, and to obtain accurate position information within the area, the robot needs to have an accurate map of the area. This circular problem is the Simultaneous Localization and Mapping (SLAM) problem. An efficient algorithm to solve it is FastSLAM, which is based on the Rao-Blackwellized particle filter. FastSLAM solves the SLAM problem for single-robot mapping using particles to represent the posterior of the robot pose and the map. Each particle of the filter possesses its own global map which is likely to be a grid map. The memory space required for these maps poses a serious limitation to the algorithm\u27s capability when the problem space is large. The problem will only get worse if the algorithm is adapted to multi-robot mapping. This thesis presents an alternate mapping algorithm that extends the single-robot FastSLAM algorithm to a multi-robot mapping algorithm that uses Absolute Space Representations (ASR) to represent the world. But each particle still maintains a local grid to map its vicinity and periodically this grid map is converted into an ASR. An ASR expresses a world in polygons requiring only a minimal amount of memory space. By using this altered mapping strategy, the problem faced in FastSLAM when mapping a large domain can be alleviated. In this algorithm, each robot maps separately, and when two robots encounter each other they exchange range and odometry readings from their last encounter to this encounter. Each robot then sets up another filter for the other robot\u27s data and incrementally updates its own map, incorporating the passed data and its own data at the same time. The passed data is processed in reverse by the receiving robot as if a virtual robot is back-tracking the path of the other robot. The algorithm is demonstrated using three data sets collected using a single robot equipped with odometry and laser-range finder sensors

Robot Mapping with Real-Time Incremental Localization Using Expectation Maximization

This research effort explores and develops a real-time sonar-based robot mapping and localization algorithm that provides pose correction within the context of a single room, to be combined with pre-existing global localization techniques, and thus produce a single, well-formed map of an unknown environment. Our algorithm implements an expectation maximization algorithm that is based on the notion of the alpha-beta functions of a Hidden Markov Model. It performs a forward alpha calculation as an integral component of the occupancy grid mapping procedure using local maps in place of a single global map, and a backward beta calculation that considers the prior local map, a limited step that enables real-time processing. Real-time localization is an extremely difficult task that continues to be the focus of much research in the field, and most advances in localization have been achieved in an off-line context. The results of our research into and implementation of realtime localization showed limited success, generating improved maps in a number of cases, but not all-a trade-off between real-time and off-line processing. However, we believe there is ample room for extension to our approach that promises a more consistently successful real-time localization algorithm

Cost optimization in AGV applications

A otimização de custos em aplicações com veículos autónomos pode ser conseguida em diversas frentes. Nesta dissertação estudam-se e comparam-se soluções a três problemas: a interface entre instalador/operador do robô; a otimização de variáveis na solução de um problema de logística; e a escolha dos sensores afetos ao sistema de navegação