RatSLAM: A hippocampal model for simultaneous localisation and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Probabilistic visual recognition of artificial landmarks for simultaneous localization and mapping

Probabilistic robotics most often applied to the problem of simultaneous localisation and mapping (SLAM), requires measures of uncertainty to accompany observations of the environment. This paper describes how uncertainty can be characterised for a vision system that locates coloured landmarks in a typical laboratory environment. The paper describes a model of the uncertainty in segmentation, the internal cameral model and the mounting of the camera on the robot. It explains the implementation of the system on a laboratory robot, and provides experimental results that show the coherence of the uncertainty model.

Multi-sensor Simultaneous Localization and Mapping

Simultaneous localization and mapping(SLAM) is a very important problem in mobile robotics. Many solutions have been proposed by different scientists during the last two decades, nevertheless few studies have considered the use of multiple sensors simultane¬ously. The solution is on combining several data sources with the aid of an Extended Kalman Filter (EKF). Two approaches are proposed. The first one is to use the ordinary EKF SLAM algorithm for each data source separately in parallel and then at the end of each step, fuse the results into one solution. Another proposed approach is the use of multiple data sources simultaneously in a single filter. The comparison of the computational com¬plexity of the two methods is also presented. The first method is almost four times faster than the second one.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

Local map update for large scale SLAM

A technique for simultaneous localisation and mapping (SLAM) for large scale scenarios is presented. This solution is based on the use of independent submaps of a limited size to map large areas. In addition, a global stochastic map, containing the links between adjacent submaps, is built. The information in both levels is corrected every time a loop is closed: local maps are updated with the information from overlapping maps, and the global stochastic map is optimised by means of constrained minimisation

Local map update for large scale SLAM

A technique for simultaneous localisation and mapping (SLAM) for large scale scenarios is presented. This solution is based on the use of independent submaps of a limited size to map large areas. In addition, a global stochastic map, containing the links between adjacent submaps, is built. The information in both levels is corrected every time a loop is closed: local maps are updated with the information from overlapping maps, and the global stochastic map is optimised by means of constrained minimisation

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.

Sistema de mapeamento e localização simultânea para inspecção subaquática

A sequente dissertação resulta do desenvolvimento de um sistema de navegação subaquático para um Remotely Operated Vehicle (ROV). A abordagem proposta consiste de um algoritmo em tempo real baseado no método de Mapeamento e Localização Simultâneo (SLAM) a partir de marcadores em ambientes marinhos não estruturados. SLAM introduz dois principais desafios: (i) reconhecimento dos marcadores provenientes dos dados raw do sensor, (ii) associação de dados. Na detecção dos marcadores foram aplicadas técnicas de visão artificial baseadas na extracção de pontos e linhas. Para testar o uso de features no visual SLAM em tempo real nas operações de inspecção subaquáticas foi desenvolvida uma plataforma modicada do RT-SLAM que integra a abordagem EKF SLAM. A plataforma é integrada em ROS framework e permite estimar a trajetória 3D em tempo real do ROV VideoRay Pro 3E até 30 fps. O sistema de navegação subaquático foi caracterizado num tanque instalado no Laboratório de Sistemas Autónomos através de um sistema stereo visual de ground truth. Os resultados obtidos permitem validar o sistema de navegação proposto para veículos subaquáticos. A trajetória adquirida pelo VideoRay em ambiente controlado é validada pelo sistema de ground truth. Dados para ambientes não estruturados, como um gasoduto, foram adquiridos e obtida respectiva trajetória realizada pelo robô. Os dados apresentados comprovam uma boa precisão e exatidão para a estimativa da posição.

Underwater slam for estructured environments using and imaging sonar

Aquesta tesi tracta sobre el problema de la navegació per a vehicles submarins autònoms que operen en entorns artificials estructurats com ara ports, canals, plataformes marines i altres escenaris similars. A partir d'una estimació precisa de la posició en aquests entorns, les capacitats dels vehicles submarins s'incrementen notablement i s'obre una porta al seu funcionament autònom. El manteniment, inspecció i vigilància d'instal lacions marines són alguns exemples de possibles aplicacions. Les principals contribucions d'aquesta tesi consisteixen per una banda en el desenvolupament de diferents sistemes de localització per a aquelles situacions on es disposa d'un mapa previ de l'entorn i per l'altra en el desenvolupament d'una nova solució al problema de la Localització i Construcció Simultània de Mapes (SLAM en les seves sigles en anglès), la finalitat del qual és fer que un vehicle autònom creï un mapa de l'entorn desconegut que el rodeja i, al mateix temps, utilitzi aquest mapa per a determinar la seva pròpia posició. S'ha escollit un sonar d'imatges d'escaneig mecànic com a sensor principal per a aquest treball tant pel seu relatiu baix cost com per la seva capacitat per produir una representació detallada de l'entorn. Per altra banda, les particularitats de la seva operació i, especialment, la baixa freqúència a la que es produeixen les mesures, constitueixen els principals inconvenients que s'han hagut d'abordar en les estratègies de localització proposades. Les solucions adoptades per aquests problemes constitueixen una altra contribució d'aquesta tesi. El desenvolupament de vehicles autònoms i el seu ús com a plataformes experimentals és un altre aspecte important d'aquest treball. Experiments portats a terme tant en el laboratori com en escenaris reals d'aplicació han proporcionat les dades necessàries per a provar i avaluar els diferents sistemes de localització proposats.

Selective submap joining SLAM for autonomous vehicles

Simultaneous Localization and Mapping (SLAM) do not result in consistent maps of large areas because of gradual increase of the uncertainty for long term missions. In addition, as the size of the map grows the computational cost increases, making SLAM solutions unsuitable for on-line applications. This thesis surveys SLAM approaches paying special attention to those approaches aimed to work on large scenarios. Special focus is given to existing underwater SLAM applications. A technique based on using independent local maps together with a global stochastic map is presented. This technique is called Selective Submap Joining SLAM (SSJS). A global map contains relative transformations between local maps, which are updated once a new loop is detected. Maps sharing several features are fused, maintaining the correlation between landmarks and vehicle's pose. The use of local maps reduces computational costs and improves map consistency as compared to state of the art techniques.

Scene Reconstruction And Understanding By RGB-D Sensors

This thesis investigates interactive scene reconstruction and understanding using RGB-D data only. Indeed, we believe that depth cameras will still be in the near future a cheap and low-power 3D sensing alternative suitable for mobile devices too. Therefore, our contributions build on top of state-of-the-art approaches to achieve advances in three main challenging scenarios, namely mobile mapping, large scale surface reconstruction and semantic modeling. First, we will describe an effective approach dealing with Simultaneous Localization And Mapping (SLAM) on platforms with limited resources, such as a tablet device. Unlike previous methods, dense reconstruction is achieved by reprojection of RGB-D frames, while local consistency is maintained by deploying relative bundle adjustment principles. We will show quantitative results comparing our technique to the state-of-the-art as well as detailed reconstruction of various environments ranging from rooms to small apartments. Then, we will address large scale surface modeling from depth maps exploiting parallel GPU computing. We will develop a real-time camera tracking method based on the popular KinectFusion system and an online surface alignment technique capable of counteracting drift errors and closing small loops. We will show very high quality meshes outperforming existing methods on publicly available datasets as well as on data recorded with our RGB-D camera even in complete darkness. Finally, we will move to our Semantic Bundle Adjustment framework to effectively combine object detection and SLAM in a unified system. Though the mathematical framework we will describe does not restrict to a particular sensing technology, in the experimental section we will refer, again, only to RGB-D sensing. We will discuss successful implementations of our algorithm showing the benefit of a joint object detection, camera tracking and environment mapping.