Creating Simplified 3D Models with High Quality Textures

This paper presents an extension to the KinectFusion algorithm which allows creating simplified 3D models with high quality RGB textures. This is achieved through (i) creating model textures using images from an HD RGB camera that is calibrated with Kinect depth camera, (ii) using a modified scheme to update model textures in an asymmetrical colour volume that contains a higher number of voxels than that of the geometry volume, (iii) simplifying dense polygon mesh model using quadric-based mesh decimation algorithm, and (iv) creating and mapping 2D textures to every polygon in the output 3D model. The proposed method is implemented in real-time by means of GPU parallel processing. Visualization via ray casting of both geometry and colour volumes provides users with a real-time feedback of the currently scanned 3D model. Experimental results show that the proposed method is capable of keeping the model texture quality even for a heavily decimated model and that, when reconstructing small objects, photorealistic RGB textures can still be reconstructed.Comment: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Page 1 -

Knowledge Representation for Robots through Human-Robot Interaction

The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing "knowledgeable" robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP 201

Sensing of complex buildings and reconstruction into photo-realistic 3D models

The 3D reconstruction of indoor and outdoor environments has received an interest only recently, as companies began to recognize that using reconstructed models is a way to generate revenue through location-based services and advertisements. A great amount of research has been done in the field of 3D reconstruction, and one of the latest and most promising applications is Kinect Fusion, which was developed by Microsoft Research. Its strong points are the real-time intuitive 3D reconstruction, interactive frame rate, the level of detail in the models, and the availability of the hardware and software for researchers and enthusiasts. A representative effort towards 3D reconstruction is the Point Cloud Library (PCL). PCL is a large scale, open project for 2D/3D image and point cloud processing. On December 2011, PCL made available an implementation of Kinect Fusion, namely KinFu. KinFu emulates the functionality provided in Kinect Fusion. However, both implementations have two major limitations: 1. The real-time reconstruction takes place only within a cube with a size of 3 meters per axis. The cube's position is fixed at the start of execution, and any object outside of this cube is not integrated into the reconstructed model. Therefore the volume that can be scanned is always limited by the size of the cube. It is possible to manually align many small-size cubes into a single large model, however this is a time-consuming and difficult task, especially when the meshes have complex topologies and high polygon count, as is the case with the meshes obtained from KinFu. 2. The output mesh does not have any color textures. There are some at-tempts to add color in the output point cloud; however, the resulting effect is not photo-realistic. Applying photo-realistic textures to a model can enhance the user experience, even when the model has a simple topology. The main goal of this project is to design and implement a system that captures large indoor environments and generates 3D photo-realistic large indoor models in real time. This report describes an extended version of the KinFu system. The extensions overcome the scalability and texture reconstruction limitations using commodity hardware and open-source software. The complete hardware setup used in this project is worth €2,000, which is comparable to the cost of a single professional laser scanner. The software is released under BSD license, which makes it completely free to use and commercialize. The system has been integrated into the open-source PCL project. The immediate benefits are three-fold: the system becomes a potential industry standard, it is maintained and extended by many developers around the world with no addition-al cost to the VCA group, and it can reduce the application development time by reusing numerous state-of-the-art algorithms