Narunas Vaskevicius

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A fast but nevertheless accurate approach for surface extraction from noisy 3D point clouds is presented. It consists of two parts, namely a plane fitting and a polygonalization step. Both exploit the sequential nature of 3D data acquisition on mobile robots in form of range images. For the plane fitting, this is used to revise the standard mathematical(More)
We present a robot-pose-registration algorithm, which is entirely based on large planar-surface patches extracted from point clouds sampled from a three-dimensional (3-D) sensor. This approach offers an alternative to the traditional point-to-point iterative-closest-point (ICP) algorithm, its point-to-plane variant, as well as newer grid-based algorithms,(More)
In this work, we utilize a recently studied more accurate range noise model for 3D sensors to derive from scratch the expressions for the optimum plane which best fits a point-cloud and for the combined covariance matrix of the plane's parameters. The parameters in question are the plane's normal and its distance to the origin. The range standard-deviation(More)
Good situation awareness is an absolute must when operating mobile robots for planetary exploration. 3D sensing and modeling data gathered by the robot are hence crucial for the operator. But standard methods based on stereo vision have their limitations, especially in scenarios where there is no or only very limited visibility, e.g., due to extreme light(More)
A fast pose-graph relaxation technique is presented for enhancing the consistency of three-dimensional (3D) maps created by registering large planar surface patches. The surface patches are extracted from point clouds sampled from a 3D range sensor. The plane-based registration method offers an alternative to the state-of-theart algorithms and provides(More)
3D sensing and modeling is increasingly important for mobile robotics in general and safety, security and rescue robotics (SSRR) in particular. To reduce the data and to allow for efficient processing, e.g., with computational geometry algorithms, it is necessary to extract surface data from 3D point clouds delivered by range sensors. A significant amount(More)
In this paper, we give an overview of the Jacobs Robotics entry to the ICRA'11 Solutions in Perception Challenge. We present our multi-pronged strategy for object recognition and localization based on the integrated geometric and visual information available from the Kinect sensor. Firstly, the range image is over-segmented using an edge-detection algorithm(More)
The recently introduced Minimum Uncertainty Maximum Consensus (MUMC) algorithm for 3D scene registration using planar-patches is tested in a large outdoor urban setting without any prior motion estimate whatsoever. With the aid of a new overlap metric based on unmatched patches, the algorithm is shown to work successfully in most cases. The absolute(More)
In previous work, the authors presented a 3D scan-registration algorithm based on minimizing the uncertainty-volume of the estimated inter-scan transform, computed by matching planar-patches extracted from a pair of 3D range-images. The method was shown to have a larger region of convergence than points-based methods like ICP. With the advent of newer(More)
This article addresses fast 3D mapping by a mobile robot in a predominantly planar environment. It is based on a novel pose registration algorithm based entirely on matching features composed of plane-segments extracted from point-clouds sampled from a 3D sensor. The approach has advantages in terms of robustness, speed and storage as compared to the voxel(More)