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Our work presents solutions to two related vexing problems in feature-based localization of ground targets in Unmanned Aerial Vehicle (UAV) images: (i) A good initial guess at the pose estimate that would speed up the convergence to the final pose estimate for each image frame in a video sequence; and (ii)Time-bounded estimation of the position of the ground target. We address both these problems...
This paper presents a 6 degrees of freedom egomotion estimation method using Iterative Closest Point (ICP) for low cost and low accuracy range cameras such as the Microsoft Kinect. Instead of Euclidean coordinates, the method uses inverse depth coordinates which better conforms to the error characteristics of raw sensor data. Novel inverse depth formulations of point-to-point and point-to-plane error...
We present a technique to estimate the egomotion of an RGB-D sensor based on rotations of functions defined on the unit sphere. In contrast to traditional approaches, our technique is not based on image features and does not require correspondences to be generated between frames of data. Instead, consecutive functions are correlated using spherical harmonic analysis. An Extended Gaussian Image (EGI),...
An RGB-D camera is a sensor which outputs range and color information about objects. Recent technological advances in this area have introduced affordable RGB-D devices in the robotics community. In this paper, we present a real-time technique for 6-DoF camera pose estimation through the incremental registration of RGB-D images. First, a set of edge features are computed from the depth and color images...
This paper presents a method for pairwise 3D alignment which solves data association by matching scan segments across scans. Generating accurate segment associations allows to run a modified version of the Iterative Closest Point (ICP) algorithm where the search for point-to-point correspondences is constrained to associated segments. The novelty of the proposed approach is in the segment matching...
The scan matching is widely used for localization and mapping of mobile robots. In this paper, a direction of data point in the scan is approximated and this is incorporated into the scan matching algorithm to improve the performance. The direction of data point is the normal direction of the least squares fitted line based on neighbors of the data point. Owing to this incorporation, the performance...
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information about the movement of the object is available, which we will combine with the visual information. Furthermore we do not only consider observations of the object, but we also take space into account which has been observed...
While Iterative Closest Point (ICP) algorithms have been successful at aligning 3D point clouds, they do not take into account constraints arising from sensor viewpoints. More recent beam-based models take into account sensor noise and viewpoint, but problems still remain. In particular, good optimization strategies are still lacking for the beam-based model. In situations of occlusion and clutter,...
ICP (Iterative Closest Point) algorithm plays an important role in sequential scan matching SLAM. In the algorithm, closest point searching is the well-recognized speed bottleneck. We realize that a searching-friendly structure already exists in the raw data sequence from range acquisition device, and the acquisition order, which we called sensor order, is naturally derived from sensor working mechanism...
Point set registration-the task of finding the best fitting alignment between two sets of point samples, is an important problem in mobile robotics. This article proposes a novel registration algorithm, based on the distance between Three-Dimensional Normal Distributions Transforms. 3D-NDT models — a sub-class of Gaussian Mixture Models with uniformly weighted, largely disjoint components, can be...
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