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This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and...
Recognition of dominant planes is an important task used in areas such as robot navigation, augmented reality, 3D reconstruction, among others. There are several approaches for recognizing planar structures, however, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds associated with the camera...
We present a novel methodology that combines stereo vision and parallel processing, based on GPU and the use of binary descriptors, for fast plane extraction. Typical stereo algorithms require an image rectification stage that has to run in a frame-to-frame basis, increasing the computational burden and with the possibility of compromising high frame rate operation. Hence, we propose to use a semi-calibrated...
In this work we address the problem of 3D mapping of environments with large textureless regions, which generates sparse 3D maps that may not represent well the mapped scene. To deal with this problem, we propose to enhance sparse 3D maps by using a superpixel-based segmentation with the aim of generating denser 3D maps of the scene in real time. This can be exploited in virtual reality applications...
Augmented reality combines real footage taken of a scene with virtual elements. However, most current methods rely on camera localisation and 3D reconstruction or point cloud generation in order to integrate augmented reality to the footage. In contrast, in this work we present a novel method to augment virtual reality to the scene based on the recognition of dominant planes in interior scenes. Our...
In this work we present initial results of a system that combines wearable technology and monocular simultaneous localisation and mapping (SLAM) for remote controlling of a low-cost micro aerial vehicle (MAV) that flies beyond the visual line-of-sight. To this purpose, as a first step, we use a state-of-the-art visual SLAM system, called ORB-SLAM, to create a 3D map of the scene. The visual data feeding...
We develop and evaluate methods based on a novel data compression strategy for visual SLAM that uses traveled trajectory analysis. Beyond compressing scene structure based purely on geometry, we aim at developing compact map representations that are useful for re-exploration while preserving scene structure. Our work is evaluated on data collected from a visual sensor and exploits the information...
In this paper we present a dense visual odometry system for RGB-D cameras performing both photometric and geometric error minimisation to estimate the camera motion between frames. Contrary to most works in the literature, we parametrise the geometric error by the inverse depth instead of the depth, which translates into a better fit of the distribution of the geometric error to the used robust cost...
One open problem in the fields of place recognition and mapping is to be able to recognise a revisited place when its appearance and layout have changed between visits. In this paper, we investigate this problem in the context of RGB-D mapping in indoor environments. We propose to segment the scene in juts (neighbourhood of 3D points with normals that stick out from the surroundings) and look at low-level...
Many applications of Micro Air Vehicles (MAVs) require them to operate in cluttered environments, flying in constrained spaces and close to obstacles. Such obstacles affect the airflow around the MAV and can thereby affect its flight characteristics. We describe a system for predicting these effects at a distance, using depth images obtained from an RGB-D sensor. Predictions are based on learning...
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