A Comparison of Line Extraction Algorithms using 2D Laser Rangefinder for Indoor Mobile Robotics

This paper presents an experimental evaluation of different line extraction algorithms on 2D laser scans for indoor environment. Six popular algorithms in mobile robotics and computer vision are selected and tested. Experiments are performed on 100 real data scans collected in an office environment with a map size of 80m /spl times/ 50m. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with the ground truth using standard statistical methods

Experimental design of autonomous vehicle using neural networks

Este artículo está relacionado con el diseño experimental e implementación de un vehículo autónomo para el transporte de mercancías o materias primas en el interior de una industria o comercio. El proyecto fue desarrollado y coordinado por la Escuela de Ingeniería Eléctrica y Electrónica de ITCA-FEPADE. Este vehículo es accionado a través de un conjunto de sensores, tales como infrarrojos, ultrasónicos y sensor LIDAR; el vehículo es capaz de detectar su entorno, y basados en ellos, alcanzar su destino mediante decisiones de un Raspberry, que, ejecutando un programa basado en red neuronal da las instrucciones a un microcontrolador Arduino, el cual impulsa los motores eléctricos utilizando una etapa de potencia basada en transistores MOSFETs. La red neuronal es un tipo de control adaptativo, que viene a sustituir a los controladores tradicionales; al igual que el ser humano, la red neuronal debe ser entrenada para un funcionamiento óptimo utilizando inteligencia artificial, tal como el método de retropropagación, en la cual la red neuronal aprende de manera supervisada, en base a patrones de entrada y salidas conocidas. El vehículo es capaz de transportar un peso de hasta 30 Kg y las tareas de carga y descarga serán realizadas por un operador humano. Debido a los componentes electrónicos a bordo del vehículo, se recomienda su operación en ambientes secos y una superficie plana. El nivel de autonomía del vehículo, se refiere a transportar la carga de un punto a otro sin acción humana directa durante su desplazamiento. Entre los campos de aplicación, se puede considerar el área logística e industrial, para el transporte de materia prima, herramientas, componentes electrónicos, telas y alimentos enlatados, entre otros

Accumulator-free Hough Transform for Sequence Collinear Points

The perception, localization, and navigation of its environment are essential for autonomous mobile robots and vehicles. For that reason, a 2D Laser rangefinder sensor is used popularly in mobile robot applications to measure the origin of the robot to its surrounding objects. The measurement data generated by the sensor is transmitted to the controller, where the data is processed by one or multiple suitable algorithms in several steps to extract the desired information. Universal Hough Transform (UHT) is one of the appropriate and popular algorithms to extract the primitive geometry such as straight line, which later will be used in the further step of data processing. However, the UHT has high computational complexity and requires the so-called accumulator array, which is less suitable for real-time applications where a high speed and low complexity computation is highly demanded. In this study, an Accumulator-free Hough Transform (AfHT) is proposed to reduce the computational complexity and eliminate the need for the accumulator array. The proposed algorithm is validated using the measurement data from a 2D laser scanner and compared to the standard Hough Transform. As a result, the extracted value of AfHT shows a good agreement with that of UHT but with a significant reduction in the complexity of the computation and the need for computer memory

LINE SEGMENTATION OF 2D LASER SCANNER POINT CLOUDS FOR INDOOR SLAM BASED ON A RANGE OF RESIDUALS

Indoor mobile laser scanning (IMLS) based on the Simultaneous Localization and Mapping (SLAM) principle proves to be the preferred method to acquire data of indoor environments at a large scale. In previous work, we proposed a backpack IMLS system containing three 2D laser scanners and an according SLAM approach. The feature-based SLAM approach solves all six degrees of freedom simultaneously and builds on the association of lines to planes. Because of the iterative character of the SLAM process, the quality and reliability of the segmentation of linear segments in the scanlines plays a crucial role in the quality of the derived poses and consequently the point clouds. The orientations of the lines resulting from the segmentation can be influenced negatively by narrow objects which are nearly coplanar with walls (like e.g. doors) which will cause the line to be tilted if those objects are not detected as separate segments. State-of-the-art methods from the robotics domain like Iterative End Point Fit and Line Tracking were found to not handle such situations well. Thus, we describe a novel segmentation method based on the comparison of a range of residuals to a range of thresholds. For the definition of the thresholds we employ the fact that the expected value for the average of residuals of n points with respect to the line is σ / √n. Our method, as shown by the experiments and the comparison to other methods, is able to deliver more accurate results than the two approaches it was tested against

Caracterización de un sensor de rango láser de bajo costo previo a una implementación de Slam

Una propiedad básica que debe poseer cualquier vehículo autónomo es su capacidad de desplazarse libremente a través de su ambiente de trabajo. La navegación robótica autónoma se consigue cuando el problema computacional denominado Localización y Mapeado Simultáneo (SLAM) es resuelto. Para conseguir esto, el robot móvil debe calcular su posición aproximada al mismo tiempo que construye y actualiza un mapa de ambiente. Tradicionalmente, se utilizan las lecturas odométricas y los rangos de los objetos circundantes obtenidos por medio de un sensor de rango láser. La estimación de la posición del robot es mejorada en la medida en que la data sensoriales recolectada, manejada y modelada apropiadamente. Debido a su rapidez, exactitud, resolución angular y amplia área de barrido, el sensor de rango láser ha sido usado en muchas aplicaciones para resolver el problema de SLAM con éxito, pero siempre existe un compromiso entre su costo y su desempeño. En este artículo se presenta una caracterización del sensor de rango láser Hokuyo URG-04LX_UG01, un sensor de bajo costo. El propósito final es establecer un modelo funcional para solucionar SLAM en interiores

A review of sensor technology and sensor fusion methods for map-based localization of service robot

Service robot is currently gaining traction, particularly in hospitality, geriatric care and healthcare industries. The navigation of service robots requires high adaptability, flexibility and reliability. Hence, map-based navigation is suitable for service robot because of the ease in updating changes in environment and the flexibility in determining a new optimal path. For map-based navigation to be robust, an accurate and precise localization method is necessary. Localization problem can be defined as recognizing the robot’s own position in a given environment and is a crucial step in any navigational process. Major difficulties of localization include dynamic changes of the real world, uncertainties and limited sensor information. This paper presents a comparative review of sensor technology and sensor fusion methods suitable for map-based localization, focusing on service robot applications

A Maximum Likelihood Approach to Extract Polylines from 2-D Laser Range Scans

Man-made environments such as households, offices, or factory floors are typically composed of linear structures. Accordingly, polylines are a natural way to accurately represent their geometry. In this paper, we propose a novel probabilistic method to extract polylines from raw 2-D laser range scans. The key idea of our approach is to determine a set of polylines that maximizes the likelihood of a given scan. In extensive experiments carried out on publicly available real-world datasets and on simulated laser scans, we demonstrate that our method substantially outperforms existing state-of-the-art approaches in terms of accuracy, while showing comparable computational requirements. Our implementation is available under https://github.com/acschaefer/ple.Comment: 9 page

VELOCITY PERCEPTION USING AVERAGE OF DISTANCE BETWEEN THE OBJECTS THERE WAS IN ENVIRONMENT

In ecological psychology, it is thought that animals and insects use visual information in exchange for distance information. In this paper, we focus attention on the mechanism of animals into consideration and address the method used to estimate the velocity of a vehicle by employing only one camera. Simulations are conducted and their accuracy is discussed

Towards a Cognitive Probabilistic Representation of Space for Mobile Robots

Robots are rapidly evolving from factory workhorses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a way that is comprehensible to humans. This paper is oriented in this direction. It suggests a hierarchical probabilistic representation of space that is based on objects. A global topological representation of places with object graphs serving as local maps is suggested. Experiments on place classification and place recognition are also reported in order to demonstrate the applicability of such a representation in the context of understanding space and thereby performing spatial cognition. Thus the theme of the work is representation for spatial cognition