Topological SLAM using a graph-matching based method on omnidirectional images

Comunicación presentada en el XI Workshop of Physical Agents, Valencia, 9-10 septiembre 2010.

Comparativa de detectores de características visuales y su aplicación al SLAM

Comunicación presentada en el X Workshop of Physical Agents, Cáceres, 10-11 septiembre 2009.

Large scale SLAM with visual features

Comunicación presentada en el X Workshop of Physical Agents, Cáceres, 10-11 septiembre 2009.

3D plane-based egomotion for SLAM on semi-structured environment

Several works deal with 3D data in SLAM problem. Data come from a 3D laser sweeping unit or a stereo camera, both providing a huge amount of data. In this paper, we detail an efficient method to extract planar patches from 3D raw data. Then, we use these patches in an ICP-like method in order to address the SLAM problem. Using ICP with planes is not a trivial task. It needs some adaptation from the original ICP. Some promising results are shown for outdoor environment.

A study of 2D features for 3D visual SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR), Taipei, Taiwan, August 29-31, 2012.

Estrategias para identificar oclusiones y planificación monocular para una mejora de la percepción visual de la escena

Esta Tesis doctoral está orientada al estudio de estrategias y técnicas para el tratamiento de oclusiones. Las oclusiones suponen uno de los principales problemas en la percepción de una escena mediante visión por computador. Las condiciones de luz, los puntos de vista con los que se captura información de la escena, las posiciones y orientaciones de los objetos presentes en la escena son algunas de las causas que provocan que los objetos puedan quedar ocluidos parcialmente. Las investigaciones expuestas en esta Tesis se pueden agrupar en función de su objetivo en dos grupos: técnicas cuya finalidad es detectar la presencia de oclusiones y estrategias que permiten mejorar la percepción de un sistema de visión por computador, aun en el caso de la presencia de oclusiones. En primer lugar, se han desarrollado una serie de técnicas orientadas a la detección de oclusiones a partir de procesos de extracción de características y de segmentación color en imágenes. Estas técnicas permiten definir qué regiones en la imagen son susceptibles de considerarse zonas de oclusión, debido a una mala percepción de la escena, como consecuencia de observarla con un mal punto de vista. Como aplicación de estas técnicas se han desarrollado algoritmos basados en la segmentación color de la imagen y en la detección de discontinuidades mediante luz estructurada. Estos algoritmos se caracterizan por no incluir conocimiento previo de la escena. En segundo lugar, se han presentado una serie de estrategias que permiten corregir y/o modificar el punto de vista de la cámara con la que se observa la escena. De esta manera, las oclusiones identificadas, mediante los métodos expuestos en la primera parte de la Tesis, y que generalmente son debidas a una mala localización de la cámara pueden ser eliminadas o atenuadas cambiando el punto de vista con el que se produce la observación. En esta misma línea se presentan dos estrategias para mejorar la posición y orientación espacial de la cámara cuando ésta se emplea para la captura de imágenes en procesos de reconocimiento. La primera de ellas se basa en la retroproyección de características obtenidas de una imagen real, a partir de una posición cualquiera, en imágenes virtuales correspondientes a las posibles posiciones que puede adoptar la cámara. Este algoritmo lleva a cabo la evaluación de un mapa de distancias entre estas características buscando en todo momento, maximizar estas distancias para garantizar un mejor punto de vista. La ventaja radica en que en ningún caso se hace necesario mover la cámara para determinar una nueva posición que mejore la percepción de la escena. La segunda de estas estrategias, busca corregir la posición de la cámara buscando la ortogonalidad. En este caso, se ha partido de la hipótesis inicial de que la mayor superficie visible siempre se suele conseguir situando la cámara ortogonalmente al plano en el que se sitúa el objeto.

3D Feature Extraction and Modelling for SLAM

Paper submitted to the 39th International Symposium on Robotics ISR 2008, Seoul, South Korea, October 15-17, 2008.

Portable 3D laser-camera calibration system with color fusion for SLAM

Nowadays, the use of RGB-D sensors have focused a lot of research in computer vision and robotics. These kinds of sensors, like Kinect, allow to obtain 3D data together with color information. However, their working range is limited to less than 10 meters, making them useless in some robotics applications, like outdoor mapping. In these environments, 3D lasers, working in ranges of 20-80 meters, are better. But 3D lasers do not usually provide color information. A simple 2D camera can be used to provide color information to the point cloud, but a calibration process between camera and laser must be done. In this paper we present a portable calibration system to calibrate any traditional camera with a 3D laser in order to assign color information to the 3D points obtained. Thus, we can use laser precision and simultaneously make use of color information. Unlike other techniques that make use of a three-dimensional body of known dimensions in the calibration process, this system is highly portable because it makes use of small catadioptrics that can be placed in a simple manner in the environment. We use our calibration system in a 3D mapping system, including Simultaneous Location and Mapping (SLAM), in order to get a 3D colored map which can be used in different tasks. We show that an additional problem arises: 2D cameras information is different when lighting conditions change. So when we merge 3D point clouds from two different views, several points in a given neighborhood could have different color information. A new method for color fusion is presented, obtaining correct colored maps. The system will be tested by applying it to 3D reconstruction.

Portable 3D laser-camera calibration system with color fusion for SLAM

Paper submitted to the 43rd International Symposium on Robotics (ISR2012), Taipei, Taiwan, Aug. 29-31, 2012.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

Using a RGB-D camera for 6DoF SLAM

This paper presents a method for fast calculation of the egomotion done by a robot using visual features. The method is part of a complete system for automatic map building and Simultaneous Localization and Mapping (SLAM). The method uses optical flow in order to determine if the robot has done a movement. If so, some visual features which do not accomplish several criteria (like intersection, unicity, etc,) are deleted, and then the egomotion is calculated. We use a state-of-the-art algorithm (TORO) in order to rectify the map and solve the SLAM problem. The proposed method provides better efficiency that other current methods.

An improvement of a SLAM RGB-D method with movement prediction derived from a study of visual features

This paper presents a method for the fast calculation of a robot’s egomotion using visual features. The method is part of a complete system for automatic map building and Simultaneous Location and Mapping (SLAM). The method uses optical flow to determine whether the robot has undergone a movement. If so, some visual features that do not satisfy several criteria are deleted, and then egomotion is calculated. Thus, the proposed method improves the efficiency of the whole process because not all the data is processed. We use a state-of-the-art algorithm (TORO) to rectify the map and solve the SLAM problem. Additionally, a study of different visual detectors and descriptors has been conducted to identify which of them are more suitable for the SLAM problem. Finally, a navigation method is described using the map obtained from the SLAM solution.

3D model based map building

Comunicación presentada en el IX Workshop de Agentes Físicos (WAF'2008), Vigo, 11-12 septiembre 2008.

Is there any evidence for the validity of diagnostic criteria used for accommodative and nonstrabismic binocular dysfunctions?

Purpose: To analyze the diagnostic criteria used in the scientific literature published in the past 25 years for accommodative and nonstrabismic binocular dysfunctions and to explore if the epidemiological analysis of diagnostic validity has been used to propose which clinical criteria should be used for diagnostic purposes. Methods: We carried out a systematic review of papers on accommodative and non-strabic binocular disorders published from 1986 to 2012 analysing the MEDLINE, CINAHL, PsycINFO and FRANCIS databases. We admitted original articles about diagnosis of these anomalies in any population. We identified 839 articles and 12 studies were included. The quality of included articles was assessed using the QUADAS-2 tool. Results: The review shows a wide range of clinical signs and cut-off points between authors. Only 3 studies (regarding accommodative anomalies) assessed diagnostic accuracy of clinical signs. Their results suggest using the accommodative amplitude and monocular accommodative facility for diagnosing accommodative insufficiency and a high positive relative accommodation for accommodative excess. The remaining 9 articles did not analyze diagnostic accuracy, assessing a diagnosis with the criteria the authors considered. We also found differences between studies in the way of considering patients’ symptomatology. 3 studies of 12 analyzed, performed a validation of a symptom survey used for convergence insufficiency. Conclusions: Scientific literature reveals differences between authors according to diagnostic criteria for accommodative and nonstrabismic binocular dysfunctions. Diagnostic accuracy studies show that there is only certain evidence for accommodative conditions. For binocular anomalies there is only evidence about a validated questionnaire for convergence insufficiency with no data of diagnostic accuracy.

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.