Exploiting graph structure in Active SLAM

Aplicando análisis provenientes de la teoría de grafos, la teoría espectral de grafos, la exploración de grafos en línea, generamos un sistema de SLAM activo que incluye la planificación de rutas bajo incertidumbre, extracción de grafos topológicos de entornos y SLAM activo \'optimo.En la planificación de trayectorias bajo incertidumbre, incluimos el análisis de la probabilidad de asociación correcta de datos. Reconociendo la naturaleza estocástica de la incertidumbre, demostramos que planificar para minimizar su valor esperado es más fiable que los actuales algoritmos de planificación de trayectorias con incertidumbre.Considerando el entorno como un conjunto de regiones convexas conectadas podemos tratar la exploración robótica como una exploración de grafos en línea. Se garantiza una cobertura total si el robot visita cada región. La mayoría de los métodos para segmentar el entorno están basados en píxeles y no garantizan que las regiones resultantes sean convexas, además pocos son algoritmos incrementales. En base a esto, modificamos un algoritmo basado en contornos en el que el entorno se representa como un conjunto de polígonos que debe segmentarse en un conjunto de polígonos pseudo convexos. El resultado es un algoritmo de segmentación que produjo regiones pseudo-convexas, robustas al ruido, estables y que obtienen un gran rendimiento en los conjuntos de datos de pruebas.La calidad de un algoritmo se puede medir en términos de cuan cercano al óptimo está su rendimiento. Con esta motivación definimos la esencia de la tarea de exploración en SLAM activo donde las únicas variables son la distancia recorrida y la calidad de la reconstrucción. Restringiendo el dominio al grafo que representa el entorno y probando la relación entre la matriz asociada a la exploración y la asociada al grafo subyacente, podemos calcular la ruta de exploración óptima.A diferencia de la mayoría de la literatura en SLAM activo, proponemos que la heurística para la exploración de grafos consiste en atravesar cada arco una vez. Demostramos que el tipo de grafos resultantes tiene un gran rendimiento con respecto a la trayectoria \'optima, con resultados superiores al 97 \% del \'optimo en algunas medidas de calidad.El algoritmo de SLAM activo TIGRE integra el algoritmo de extracción de grafos propuesto con nuestra versión del algoritmo de exploración incremental que atraviesa cada arco una vez. Nuestro algoritmo se basa en una modificación del algoritmo clásico de Tarry para la búsqueda en laberintos que logra el l\'imite inferior en la aproximación para un algoritmo incremental. Probamos nuestro sistema incremental en un escenario de exploración típico y demostramos que logra un rendimiento similar a los métodos fuera de línea y también demostramos que incluso el método \'optimo que visita todos los nodos calculado fuera de línea tiene un peor rendimiento que el nuestro.<br /

Topological Navigation of Simulated Robots using Occupancy Grid

Formerly I presented a metric navigation method in the Webots mobile robot simulator. The navigating Khepera-like robot builds an occupancy grid of the environment and explores the square-shaped room around with a value iteration algorithm. Now I created a topological navigation procedure based on the occupancy grid process. The extension by a skeletonization algorithm results a graph of important places and the connecting routes among them. I also show the significant time profit gained during the process

Indoor Exploration and Simultaneous Trolley Collection Through Task-Oriented Environment Partitioning

In this paper, we present a simultaneous exploration and object search framework for the application of autonomous trolley collection. For environment representation, a task-oriented environment partitioning algorithm is presented to extract diverse information for each sub-task. First, LiDAR data is classified as potential objects, walls, and obstacles after outlier removal. Segmented point clouds are then transformed into a hybrid map with the following functional components: object proposals to avoid missing trolleys during exploration; room layouts for semantic space segmentation; and polygonal obstacles containing geometry information for efficient motion planning. For exploration and simultaneous trolley collection, we propose an efficient exploration-based object search method. First, a traveling salesman problem with precedence constraints (TSP-PC) is formulated by grouping frontiers and object proposals. The next target is selected by prioritizing object search while avoiding excessive robot backtracking. Then, feasible trajectories with adequate obstacle clearance are generated by topological graph search. We validate the proposed framework through simulations and demonstrate the system with real-world autonomous trolley collection tasks

SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain

We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher success rate during exploration

Recognising, Representing and Mapping Natural Features in Unstructured Environments

This thesis addresses the problem of building statistical models for multi-sensor perception in unstructured outdoor environments. The perception problem is divided into three distinct tasks: recognition, representation and association. Recognition is cast as a statistical classification problem where inputs are images or a combination of images and ranging information. Given the complexity and variability of natural environments, this thesis investigates the use of Bayesian statistics and supervised dimensionality reduction to incorporate prior information and fuse sensory data. A compact probabilistic representation of natural objects is essential for many problems in field robotics. This thesis presents techniques for combining non-linear dimensionality reduction with parametric learning through Expectation Maximisation to build general representations of natural features. Once created these models need to be rapidly processed to account for incoming information. To this end, techniques for efficient probabilistic inference are proposed. The robustness of localisation and mapping algorithms is directly related to reliable data association. Conventional algorithms employ only geometric information which can become inconsistent for large trajectories. A new data association algorithm incorporating visual and geometric information is proposed to improve the reliability of this task. The method uses a compact probabilistic representation of objects to fuse visual and geometric information for the association decision. The main contributions of this thesis are: 1) a stochastic representation of objects through non-linear dimensionality reduction; 2) a landmark recognition system using a visual and ranging sensors; 3) a data association algorithm combining appearance and position properties; 4) a real-time algorithm for detection and segmentation of natural objects from few training images and 5) a real-time place recognition system combining dimensionality reduction and Bayesian learning. The theoretical contributions of this thesis are demonstrated with a series of experiments in unstructured environments. In particular, the combination of recognition, representation and association algorithms is applied to the Simultaneous Localisation and Mapping problem (SLAM) to close large loops in outdoor trajectories, proving the benefits of the proposed methodology