3D mapping and path planning from range data

This thesis reports research on mapping, terrain classification and path planning. These are classical problems in robotics, typically studied independently, and here we link such problems by framing them within a common proprioceptive modality, that of three-dimensional laser range scanning. The ultimate goal is to deliver navigation paths for challenging mobile robotics scenarios. For this reason we also deliver safe traversable regions from a previously computed globally consistent map. We first examine the problem of registering dense point clouds acquired at different instances in time. We contribute with a novel range registration mechanism for pairs of 3D range scans using point-to-point and point-to-line correspondences in a hierarchical correspondence search strategy. For the minimization we adopt a metric that takes into account not only the distance between corresponding points, but also the orientation of their relative reference frames. We also propose FaMSA, a fast technique for multi-scan point cloud alignment that takes advantage of the asserted point correspondences during sequential scan matching, using the point match history to speed up the computation of new scan matches. To properly propagate the model of the sensor noise and the scan matching, we employ first order error propagation, and to correct the error accumulation from local data alignment, we consider the probabilistic alignment of 3D point clouds using a delayed-state Extended Information Filter (EIF). In this thesis we adapt the Pose SLAM algorithm to the case of 3D range mapping, Pose SLAM is the variant of SLAM where only the robot trajectory is estimated and where sensor data is solely used to produce relative constraints between robot poses. These dense mapping techniques are tested in several scenarios acquired with our 3D sensors, producing impressively rich 3D environment models. The computed maps are then processed to identify traversable regions and to plan navigation sequences. In this thesis we present a pair of methods to attain high-level off-line classification of traversable areas, in which training data is acquired automatically from navigation sequences. Traversable features came from the robot footprint samples during manual robot motion, allowing us to capture terrain constrains not easy to model. Using only some of the traversed areas as positive training samples, our algorithms are tested in real scenarios to find the rest of the traversable terrain, and are compared with a naive parametric and some variants of the Support Vector Machine. Later, we contribute with a path planner that guarantees reachability at a desired robot pose with significantly lower computation time than competing alternatives. To search for the best path, our planner incrementally builds a tree using the A* algorithm, it includes a hybrid cost policy to efficiently expand the search tree, combining random sampling from the continuous space of kinematically feasible motion commands with a cost to goal metric that also takes into account the vehicle nonholonomic constraints. The planer also allows for node rewiring, and to speed up node search, our method includes heuristics that penalize node expansion near obstacles, and that limit the number of explored nodes. The method book-keeps visited cells in the configuration space, and disallows node expansion at those configurations in the first full iteration of the algorithm. We validate the proposed methods with experiments in extensive real scenarios from different very complex 3D outdoors environments, and compare it with other techniques such as the A*, RRT and RRT* algorithms.Esta tesis reporta investigación sobre el mapeo, clasificación de terreno y planificación de trayectorias. Estos son problemas clásicos en robótica los cuales generalmente se estudian de forma independiente, aquí se vinculan enmarcandolos con una modalidad propioceptiva común: un láser de rango 3D. El objetivo final es ofrecer trayectorias de navegación para escenarios complejos en el marco de la robótica móvil. Por esta razón también entregamos regiones transitables en un mapa global consistente calculado previamente. Primero examinamos el problema de registro de nubes de puntos adquiridas en diferentes instancias de tiempo. Contribuimos con un novedoso mecanismo de registro de pares de imagenes de rango 3D usando correspondencias punto a punto y punto a línea, en una estrategia de búsqueda de correspondencias jerárquica. Para la minimización optamos por una metrica que considera no sólo la distancia entre puntos, sino también la orientación de los marcos de referencia relativos. También proponemos FAMSA, una técnica para el registro rápido simultaneo de multiples nubes de puntos, la cual aprovecha las correspondencias de puntos obtenidas durante el registro secuencial, usando inicialmente la historia de correspondencias para acelerar el cálculo de las correspondecias en los nuevos registros de imagenes. Para propagar adecuadamente el modelo del ruido del sensor y del registro de imagenes, empleamos la propagación de error de primer orden, y para corregir el error acumulado del registro local, consideramos la alineación probabilística de nubes de puntos 3D utilizando un Filtro Extendido de Información de estados retrasados. En esta tesis adaptamos el algóritmo Pose SLAM para el caso de mapas con imagenes de rango 3D, Pose SLAM es la variante de SLAM donde solamente se estima la trayectoria del robot, usando los datos del sensor como restricciones relativas entre las poses robot. Estas técnicas de mapeo se prueban en varios escenarios adquiridos con nuestros sensores 3D produciendo modelos 3D impresionantes. Los mapas obtenidos se procesan para identificar regiones navegables y para planificar secuencias de navegación. Presentamos un par de métodos para lograr la clasificación de zonas transitables fuera de línea. Los datos de entrenamiento se adquieren de forma automática usando secuencias de navegación obtenidas manualmente. Las características transitables se captan de las huella de la trayectoria del robot, lo cual permite capturar restricciones del terreno difíciles de modelar. Con sólo algunas de las zonas transitables como muestras de entrenamiento positivo, nuestros algoritmos se prueban en escenarios reales para encontrar el resto del terreno transitable. Los algoritmos se comparan con algunas variantes de la máquina de soporte de vectores (SVM) y una parametrizacion ingenua. También, contribuimos con un planificador de trayectorias que garantiza llegar a una posicion deseada del robot en significante menor tiempo de cálculo a otras alternativas. Para buscar el mejor camino, nuestro planificador emplea un arbol de busqueda incremental basado en el algoritmo A*. Incluimos una póliza de coste híbrido para crecer de manera eficiente el árbol, combinando el muestro aleatorio del espacio continuo de comandos cinemáticos del robot con una métrica de coste al objetivo que también concidera las cinemática del robot. El planificador además permite reconectado de nodos, y, para acelerar la búsqueda de nodos, se incluye una heurística que penaliza la expansión de nodos cerca de los obstáculos, que limita el número de nodos explorados. El método conoce las céldas que ha visitado del espacio de configuraciones, evitando la expansión de nodos en configuraciones que han sido vistadas en la primera iteración completa del algoritmo. Los métodos propuestos se validán con amplios experimentos con escenarios reales en diferentes entornos exteriores, asi como su comparación con otras técnicas como los algoritmos A*, RRT y RRT*.Postprint (published version

HRA*: hybrid randomized path planning for complex 3D environments

We propose HRA*, a new randomized path planner for complex 3D environments. The method is a modified A* algorithm that uses a hybrid node expansion technique that combines a random exploration of the action space meeting vehicle kinematic constraints with a cost to goal metric that considers only kinematically feasible paths to the goal. The method includes also a series of heuristics to accelerate the search time. These include a cost penalty near obstacles, and a filter to prevent revisiting configurations. The performance of the method is compared against A*, RRT and RRT* in a series of challenging 3D outdoor datasets. HRA* is shown to outperform all of them in computation time, and delivering shorter paths than A* and RRPostprint (author's final draft

HRA*: hybrid randomized path planning for complex 3D environments

We propose HRA*, a new randomized path planner for complex 3D environments. The method is a modified A* algorithm that uses a hybrid node expansion technique that combines a random exploration of the action space meeting vehicle kinematic constraints with a cost to goal metric that considers only kinematically feasible paths to the goal. The method includes also a series of heuristics to accelerate the search time. These include a cost penalty near obstacles, and a filter to prevent revisiting configurations. The performance of the method is compared against A*, RRT and RRT* in a series of challenging 3D outdoor datasets. HRA* is shown to outperform all of them in computation time, and delivering shorter paths than A* and R

Terrain classification in complex three-dimensional outdoor environments

This paper presents two techniques to detect and classify navigable terrain in complex three-dimensional (3D) environments. The first method is a low level on-line mechanism aimed at detecting obstacles and holes at a fast frame rate using a time-of-flight camera as the main sensor. The second technique is a high-level offline classification mechanism that learns traversable regions from larger 3D point clouds acquired with a laser range scanner. We approach the problem using Gaussian processes as a regression tool, in which the terrain parameters are learned, and also for classification, using samples from traversed areas to build the traversable terrain class. The two methods are compared against unsupervised classification, and sample trajectories are generated in the classified areas using a nonholonomic path planner. We show results of both the low-level and the high-level terrain classification approaches in simulations and in real-time navigation experiments using a Segway RMP400 robot. (C) 2014 Wiley Periodicals, Inc.Peer ReviewedPostprint (author’s final draft

Terrain classification in complex three-dimensional outdoor environments

This paper presents two techniques to detect and classify navigable terrain in complex three-dimensional (3D) environments. The first method is a low level on-line mechanism aimed at detecting obstacles and holes at a fast frame rate using a time-of-flight camera as the main sensor. The second technique is a high-level offline classification mechanism that learns traversable regions from larger 3D point clouds acquired with a laser range scanner. We approach the problem using Gaussian processes as a regression tool, in which the terrain parameters are learned, and also for classification, using samples from traversed areas to build the traversable terrain class. The two methods are compared against unsupervised classification, and sample trajectories are generated in the classified areas using a nonholonomic path planner. We show results of both the low-level and the high-level terrain classification approaches in simulations and in real-time navigation experiments using a Segway RMP400 robot. (C) 2014 Wiley Periodicals, Inc.Peer Reviewe

Calibration of an outdoor distributed camera network with a 3D point cloud

Outdoor camera networks are becoming ubiquitous in critical urban areas of the largest cities around the world. Although current applications of camera networks are mostly tailored to video surveillance, recent research projects are exploiting their use to aid robotic systems in people-assisting tasks. Such systems require precise calibration of the internal and external parameters of the distributed camera network. Despite the fact that camera calibration has been an extensively studied topic, the development of practical methods for user-assisted calibration that minimize user intervention time and maximize precision still pose significant challenges. These camera systems have non-overlapping fields of view, are subject to environmental stress, and are likely to suffer frequent recalibration. In this paper, we propose the use of a 3D map covering the area to support the calibration process and develop an automated method that allows quick and precise calibration of a large camera network. We present two cases of study of the proposed calibration method: one is the calibration of the Barcelona Robot Lab camera network, which also includes direct mappings (homographies) between image coordinates and world points in the ground plane (walking areas) to support person and robot detection and localization algorithms. The second case consist of improving the GPS positioning of geo-tagged images taken with a mobile device in the Facultat de Matematiques i Estadistica (FME) patio at the Universitat Politecnica de Catalunya (UPC).Peer Reviewe