Guidance of robot arms using depth data from RGB-D camera

Image Based Visual Servoing (IBVS) is a robotic control scheme based on vision. This scheme uses only the visual information obtained from a camera to guide a robot from any robot pose to a desired one. However, IBVS requires the estimation of different parameters that cannot be obtained directly from the image. These parameters range from the intrinsic camera parameters (which can be obtained from a previous camera calibration), to the measured distance on the optical axis between the camera and visual features, it is the depth. This paper presents a comparative study of the performance of D-IBVS estimating the depth from three different ways using a low cost RGB-D sensor like Kinect. The visual servoing system has been developed over ROS (Robot Operating System), which is a meta-operating system for robots. The experiments prove that the computation of the depth value for each visual feature improves the system performance.

A Comparative Study of Registration Methods for RGB-D Video of Static Scenes

The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.

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.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Análisis comparativo de métodos de calibrado para sensores RGB-D y su influencia en el registro de múltiples vistas

Los sensores de propósito general RGB-D son dispositivos capaces de proporcionar información de color y de profundidad de la escena. Debido al amplio rango de aplicación que tienen estos sensores, despiertan gran interés en múltiples áreas, provocando que en algunos casos funcionen al límite de sensibilidad. Los métodos de calibración resultan más importantes, si cabe, para este tipo de sensores para mejorar la precisión de los datos adquiridos. Por esta razón, resulta de enorme transcendencia analizar y estudiar el calibrado de estos sensores RGBD de propósito general. En este trabajo se ha realizado un estudio de las diferentes tecnologías empleadas para determinar la profundidad, siendo la luz estructurada y el tiempo de vuelo las más comunes. Además, se ha analizado y estudiado aquellos parámetros del sensor que influyen en la obtención de los datos con precisión adecuada dependiendo del problema a tratar. El calibrado determina, como primer elemento del proceso de visión, los parámetros característicos que definen un sistema de visión artificial, en este caso, aquellos que permiten mejorar la exactitud y precisión de los datos aportados. En este trabajo se han analizado tres algoritmos de calibración, tanto de propósito general como de propósito específico, para llevar a cabo el proceso de calibrado de tres sensores ampliamente utilizados: Microsoft Kinect, PrimeSense Carmine 1.09 y Microsoft Kinect v2. Los dos primeros utilizan la tecnología de luz estructurada para determinar la profundidad, mientras que el tercero utiliza tiempo de vuelo. La experimentación realizada permite determinar de manera cuantitativa la exactitud y la precisión de los sensores y su mejora durante el proceso de calibrado, aportando los mejores resultados para cada caso. Finalmente, y con el objetivo de mostrar el proceso de calibrado en un sistema de registro global, diferentes pruebas han sido realizadas con el método de registro µ-MAR. Se ha utilizado inspección visual para determinar el comportamiento de los datos de captura corregidos según los resultados de los diferentes algoritmos de calibrado. Este hecho permite observar la importancia de disponer de datos exactos para ciertas aplicaciones como el registro 3D de una escena.