CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.

Analysis of the "Sonar Hopf" Cochlea

The "Sonar Hopf" cochlea is a recently much advertised engineering design of an auditory sensor. We analyze this approach based on a recent description by its inventors Hamilton, Tapson, Rapson, Jin, and van Schaik, in which they exhibit the "Sonar Hopf" model, its analysis and the corresponding hardware in detail. We identify problems in the theoretical formulation of the model and critically examine the claimed coherence between the described model, the measurements from the implemented hardware, and biological data.

Comparação de dois métodos geofísicos acústicos no mapeamento de fundos submersos: estudo de caso no Mar de Cananéia, Sistema estuarino de Cananéia-Iguape (SP)

Os estuários são ambientes altamente dinâmicos e concentram a maior parte da população mundial em seu entorno. São ambientes complexos que necessitam de uma gama de estudos. Nesse contexto, este trabalho visa contribuir para o entendimento dos estuários lagunares, tendo como objetivo comparar duas ferramentas geofísicas acústicas no mapeamento de uma porção submersa do Mar de Cananéia que está inserido no Sistema Estuarino Lagunar de Cananéia-Iguape (SP). Os equipamentos utilizados nesta pesquisa são o Sonar de Varredura Lateral e o Sistema Acústico de Classificação de Fundo RoxAnn, através da parametrização de amostras de fundo. A comparação do padrão acústico do Sonar de Varredura Lateral com as amostras de fundo da região permitiu o reconhecimento de 6 tipos distintos de padrões acústicos e a relação positiva com o diâmetro médio do grão foi de 50%. A comparação da resposta acústica do Sistema Acústico de Classificação de Fundo RoxAnn com o diâmetro médio do grão foi igualmente de 50%. Isto deve-se ao fato de que os valores produzidos pelo eco 1 e pelo eco 2 deste equipamento mostram que, por ser um mono-feixe e por analisar valores de intensidade do retorno acústico, o equipamento em questão pode responder a outros fatores ambientais que não seja somente o diâmetro médio do grão. Ao comparar a resposta acústica do Sonar de Varredura Lateral com o Sistema Acústico de Classificação de fundo RoxAnn obteve-se um resultado positivo de 93%. Isto pode ser explicado pelo fato de o Sonar de Varredura Lateral gerar uma imagem acústica do fundo. Em locais onde tem-se amostra e os valores do eco 1 e do eco 2 do Sistema Acústico de Classificação de Fundo RoxAnn são altos, pode-se associar a esses locais a influência da compactação dos sedimentos finos através da análise das imagens do Sonar de Varredura Lateral. Por meio da comparação destes dois métodos foi possível estabelecer um intervalo de valores para o eco 1 que pode ser associado ao diâmetro médio do grão. Assim, valores entre 0.170 a 0.484 milivolts podem ser associados a sedimentos finos com granulometria até areia fina. Valores entre 0.364 a 0.733 podem ser associados a sedimentos de granulometria entre areia fina a média. Valores acima de 0.805 milivolts até 1.585 milivolts podem ser associados a sedimentos mais grossos como carbonatos biodetríticos ou areias grossas. E, por fim, valores acima de 2.790 milivolts podem ser associados a afloramentos rochosos.

Padrões geoacústicos de fundo e sub-fundo na Baía de Sepetiba: implicações no conhecimento da geologia do holoceno

A Baía de Sepetiba, localizada entre a Baía de Guanabara e a Baía de Ilha Grande, Estado do Rio de Janeiro, está inserida em um cenário estratégico para o desenvolvimento econômico do Estado. Isto ocorre devido ao aumento da concentração populacional, que está diretamente relacionado com o turismo, com a presença de portos e de áreas industriais. Sendo assim, se faz necessário estudar sua estrutura geológica e dinâmica sedimentar para entender sua evolução ao longo do tempo e para uma utilização mais racional desta área. Utilizando-se da sísmica rasa de alta resolução e da sonografia de varredura lateral juntamente com dados pretéritos de amostragem superficial de sedimentos, o presente trabalho tem como objetivo principal analisar sua geologia holocênica. A investigação, em subsuperfície, da geologia estrutural e sedimentar dessa baía, através da interpretação de 09 perfis sísmicos, baseada na determinação de diferentes tipos de ecotexturas, revelou a presença de diferentes pacotes sedimentares depositados ao longo do Holoceno. Ao todo, foram encontrados 15 tipos de ecotexturas perfazendo 14 camadas sedimentares, que estão relacionados em 4 Grupos de acordo com sua distribuição. Já a investigação em superfície através dos registros sonográficos, baseada nos diferentes graus de reflexão acústica (backscattering) e parametrizada pelos dados de amostragem direta pretérita, identificou 6 padrões sonográficos distintos. Com isso foi confeccionado um novo mapa de distribuição textural dos sedimentos superficiais da Baía de Sepetiba. Com a correlação dos dados de sísmica rasa com os dados sonográficos, foi possível ainda sugerir a provável existência de neotectonismo na área de estudo.

A proposal to estimate the motion of an underwater vehicle through visual mosaicking

This thesis proposes a solution to the problem of estimating the motion of an Unmanned Underwater Vehicle (UUV). Our approach is based on the integration of the incremental measurements which are provided by a vision system. When the vehicle is close to the underwater terrain, it constructs a visual map (so called "mosaic") of the area where the mission takes place while, at the same time, it localizes itself on this map, following the Concurrent Mapping and Localization strategy. The proposed methodology to achieve this goal is based on a feature-based mosaicking algorithm. A down-looking camera is attached to the underwater vehicle. As the vehicle moves, a sequence of images of the sea-floor is acquired by the camera. For every image of the sequence, a set of characteristic features is detected by means of a corner detector. Then, their correspondences are found in the next image of the sequence. Solving the correspondence problem in an accurate and reliable way is a difficult task in computer vision. We consider different alternatives to solve this problem by introducing a detailed analysis of the textural characteristics of the image. This is done in two phases: first comparing different texture operators individually, and next selecting those that best characterize the point/matching pair and using them together to obtain a more robust characterization. Various alternatives are also studied to merge the information provided by the individual texture operators. Finally, the best approach in terms of robustness and efficiency is proposed. After the correspondences have been solved, for every pair of consecutive images we obtain a list of image features in the first image and their matchings in the next frame. Our aim is now to recover the apparent motion of the camera from these features. Although an accurate texture analysis is devoted to the matching pro-cedure, some false matches (known as outliers) could still appear among the right correspon-dences. For this reason, a robust estimation technique is used to estimate the planar transformation (homography) which explains the dominant motion of the image. Next, this homography is used to warp the processed image to the common mosaic frame, constructing a composite image formed by every frame of the sequence. With the aim of estimating the position of the vehicle as the mosaic is being constructed, the 3D motion of the vehicle can be computed from the measurements obtained by a sonar altimeter and the incremental motion computed from the homography. Unfortunately, as the mosaic increases in size, image local alignment errors increase the inaccuracies associated to the position of the vehicle. Occasionally, the trajectory described by the vehicle may cross over itself. In this situation new information is available, and the system can readjust the position estimates. Our proposal consists not only in localizing the vehicle, but also in readjusting the trajectory described by the vehicle when crossover information is obtained. This is achieved by implementing an Augmented State Kalman Filter (ASKF). Kalman filtering appears as an adequate framework to deal with position estimates and their associated covariances. Finally, some experimental results are shown. A laboratory setup has been used to analyze and evaluate the accuracy of the mosaicking system. This setup enables a quantitative measurement of the accumulated errors of the mosaics created in the lab. Then, the results obtained from real sea trials using the URIS underwater vehicle are shown.

Levantamiento batimétrico y medición de parámetros físico-químicos en el Lago de Ilopango, El Salvador

Esta investigación presenta resultados del levantamiento batimétrico exploratorio realizado en el Lago de Ilopango. El objetivo principal es cuantificar las profundidades utilizando la propagación acústica de pulsos generados a partir de un sonar monohaz, para generar una carta batimétrica, un modelo de elevación digital y establecer una línea base del régimen de estratifición físico-químico de la columna de agua, como herramienta para establecer futuros programas de monitoreo y caracterización del Lago de Ilopango. En este trabajo se registro la profundidad en 279,148 puntos separados cada tres metros y distribuido en una malla 36 perfiles que permitieron identificar siete estructuras intracaldéricas, probablemente asociados a los últimos cuatro episodios eruptivos. Por otra parte, las zonas de mayor profundidad se registraron principalmente al oeste de las Islas Cerros Quemados, frente a las Puntas de La Península y Tenango, con profundidades máximas de 235.9 ± 4.7 m y un volumen estimado de 9.97km3. El modelo 3D de la caldera, refleja una morfometría con un fondo considerablemente plano y grandes pendientes en las orillas. Por otra parte, los resultados observacionales a partir de los cuarenta perfiles que conformaron la malla de medición de la temperatura, conductividad y pH, indican que las variaciones espaciales y temporales en la columna de agua producto calentamiento del agua superficial se propaga hacia las capas más profundas favoreciendo la estratificación térmica directa que teniendo un papel importante en la regulación de la mezcla vertical en el lago. Los valores de pH son relativamente estables entre 8.0 a 8.8; los máximos observados de 9.7, posiblemente relacionados a la actividad fotosintética del fitoplancton en los meses de mayo a junio, cuando ocurre la proliferación de microalgas, cambiando la tonalidad del agua de lago. El contenido promedio de sales disueltas fue medido por intermedio de la conductividad eléctrica realizadas en 15 perfiles de 160 m de profundidad. En superficie en marzo de 2015 a 26°C, los valores registrados fueron de 1.9185µS.cm-1, 2.0479 µS.cm-1 en 12 perfiles en mayo de 2015 y 1.9108 µS.cm-1 en 13 perfiles en Enero de 2016.

Mapping of indoor environments by robots using low-cost vision sensors

For robots to operate in human environments they must be able to make their own maps because it is unrealistic to expect a user to enter a map into the robot’s memory; existing floorplans are often incorrect; and human environments tend to change. Traditionally robots have used sonar, infra-red or laser range finders to perform the mapping task. Digital cameras have become very cheap in recent years and they have opened up new possibilities as a sensor for robot perception. Any robot that must interact with humans can reasonably be expected to have a camera for tasks such as face recognition, so it makes sense to also use the camera for navigation. Cameras have advantages over other sensors such as colour information (not available with any other sensor), better immunity to noise (compared to sonar), and not being restricted to operating in a plane (like laser range finders). However, there are disadvantages too, with the principal one being the effect of perspective. This research investigated ways to use a single colour camera as a range sensor to guide an autonomous robot and allow it to build a map of its environment, a process referred to as Simultaneous Localization and Mapping (SLAM). An experimental system was built using a robot controlled via a wireless network connection. Using the on-board camera as the only sensor, the robot successfully explored and mapped indoor office environments. The quality of the resulting maps is comparable to those that have been reported in the literature for sonar or infra-red sensors. Although the maps are not as accurate as ones created with a laser range finder, the solution using a camera is significantly cheaper and is more appropriate for toys and early domestic robots.

Topological SLAM using neighbourhood information of places

Perceptual aliasing makes topological navigation a difficult task. In this paper we present a general approach for topological SLAM~(simultaneous localisation and mapping) which does not require motion or odometry information but only a sequence of noisy measurements from visited places. We propose a particle filtering technique for topological SLAM which relies on a method for disambiguating places which appear indistinguishable using neighbourhood information extracted from the sequence of observations. The algorithm aims to induce a small topological map which is consistent with the observations and simultaneously estimate the location of the robot. The proposed approach is evaluated using a data set of sonar measurements from an indoor environment which contains several similar places. It is demonstrated that our approach is capable of dealing with severe ambiguities and, and that it infers a small map in terms of vertices which is consistent with the sequence of observations.

Robot navigation in sensor space

This thesis investigates the problem of robot navigation using only landmark bearings. The proposed system allows a robot to move to a ground target location specified by the sensor values observed at this ground target posi- tion. The control actions are computed based on the difference between the current landmark bearings and the target landmark bearings. No Cartesian coordinates with respect to the ground are computed by the control system. The robot navigates using solely information from the bearing sensor space. Most existing robot navigation systems require a ground frame (2D Cartesian coordinate system) in order to navigate from a ground point A to a ground point B. The commonly used sensors such as laser range scanner, sonar, infrared, and vision do not directly provide the 2D ground coordi- nates of the robot. The existing systems use the sensor measurements to localise the robot with respect to a map, a set of 2D coordinates of the objects of interest. It is more natural to navigate between the points in the sensor space corresponding to A and B without requiring the Cartesian map and the localisation process. Research on animals has revealed how insects are able to exploit very limited computational and memory resources to successfully navigate to a desired destination without computing Cartesian positions. For example, a honeybee balances the left and right optical flows to navigate in a nar- row corridor. Unlike many other ants, Cataglyphis bicolor does not secrete pheromone trails in order to find its way home but instead uses the sun as a compass to keep track of its home direction vector. The home vector can be inaccurate, so the ant also uses landmark recognition. More precisely, it takes snapshots and compass headings of some landmarks. To return home, the ant tries to line up the landmarks exactly as they were before it started wandering. This thesis introduces a navigation method based on reflex actions in sensor space. The sensor vector is made of the bearings of some landmarks, and the reflex action is a gradient descent with respect to the distance in sensor space between the current sensor vector and the target sensor vec- tor. Our theoretical analysis shows that except for some fully characterized pathological cases, any point is reachable from any other point by reflex action in the bearing sensor space provided the environment contains three landmarks and is free of obstacles. The trajectories of a robot using reflex navigation, like other image- based visual control strategies, do not correspond necessarily to the shortest paths on the ground, because the sensor error is minimized, not the moving distance on the ground. However, we show that the use of a sequence of waypoints in sensor space can address this problem. In order to identify relevant waypoints, we train a Self Organising Map (SOM) from a set of observations uniformly distributed with respect to the ground. This SOM provides a sense of location to the robot, and allows a form of path planning in sensor space. The navigation proposed system is analysed theoretically, and evaluated both in simulation and with experiments on a real robot.

POOR ICARUS CHARMIAN : Review of The Life and Myth of Charmian Clift by Nadia Wheatley (2001) online

Online Review

Visual motion estimation for an autonomous underwater reef monitoring robot

Performing reliable localisation and navigation within highly unstructured underwater coral reef environments is a difficult task at the best of times. Typical research and commercial underwater vehicles use expensive acoustic positioning and sonar systems which require significant external infrastructure to operate effectively. This paper is focused on the development of a robust vision-based motion estimation technique using low-cost sensors for performing real-time autonomous and untethered environmental monitoring tasks in the Great Barrier Reef without the use of acoustic positioning. The technique is experimentally shown to provide accurate odometry and terrain profile information suitable for input into the vehicle controller to perform a range of environmental monitoring tasks.