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In many mobile robotics applications involving exploration of unknown environments, it would be extremely useful to provide human operators with a real-time 3D visualization of the environment the robot is exploring. Although a great deal of progress has been made in the separate fields of photorealistic structure from motion and realtime vision-based robot localization and mapping (SLAM), the ultimate...
This paper presents a novel technique to align partial 3D reconstructions of the seabed acquired by a stereo camera mounted on an autonomous underwater vehicle. Vehicle localization and seabed mapping is performed simultaneously by means of an Extended Kalman Filter. Passive landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot...
Building an accurate three dimensional map is an important task for autonomous localisation and navigation. In a sequential approach to reconstruction from video streams, we show how adding prior knowledge about camera motion improves reconstruction accuracy, obtaining a more precise trajectory estimation and preventing failures over time. We add a smoothing penalty on camera trajectory and the smoothing...
Many visually-guided robotic systems rely on stereo video data streams to obtain surface models of environmental structure. However stereo video-based 3D point clouds are noisier than those produced from laser-based scanners and are subject to areas of sparse point information corresponding to textureless or specular surfaces. This complicates the process of constructing polygonal meshes from these...
Robot egomotion can be estimated from an acquired video stream up to the scale of the scene. To remove this uncertainty (and obtain true egomotion), a distance within the scene needs to be known. If no a priori knowledge on the scene is assumed, the usual solution is to derive "in some way" the initial distance from the camera to a target object. This paper proposes a new, very simple way...
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