1,721,016 research outputs found
Metric-topological maps from laser scans adjusted with incremental tree network optimizer
No full textSeveral adaptations of maximum likelihood approaches to incremental map learning have been proposed recently. In particular, an incremental network optimizer based on stochastic gradient descent provides
a fast and easy-to-implement solution to the problem. In this paper, we illustrate two map builders that process laser scans in order to extract the constraint network for the optimization algorithm. The first algorithm builds a map in the form of a collection of scans corresponding to a subset of the poses of a robot moving in the environment. Even though such a solution has the advantage of simplicity, it requires careful processing of data associations. After a preliminary selection of pose constraints candidates, a relative pose is computed through standard scan matching techniques. The second map builder stores a hybrid metric-topological representation: the map consists of a graph whose nodes contain local occupancy grid maps and whose edges are labeled with the relative pose between pairs of nodes. Each patch map summarizes the information of consecutive raw scans and such a richer representation better solves loop closure. Association between pairs of local maps is then performed and tested using correlation-based techniques. Our aim is to illustrate the effectiveness of a tree network optimizer integrated with simple methods for data association. Experiments reported in the paper show that a compact system integrating the optimizer and one of two versions of the map builder works reasonably well with commonly used benchmarks
Rotation Estimation Based on Anisotropic Angular Radon Spectrum
Full text linkIn this letter, we present the anisotropic Angular Radon Spectrum (ARS), a novel feature for global estimation of rotation in a two dimension space. ARS effectively describes collinearity of points and has the properties of translation-invariance and shift-rotation. We derive the analytical expression of ARS for Gaussian Mixture Models (GMM) representing point clouds where the Gaussian kernels have arbitrary covariances. Furthermore, we developed a preliminary procedure for simplification of GMM suitable for efficient computation of ARS. Rotation between point clouds is estimated by searching of maximum of correlation between their spectra. Correlation is efficiently computed from Fourier series expansion of ARS. Experiments on datasets of distorted object shapes, laser scans and on robotic mapping datasets assess the accuracy and robustness to noise in global rotation estimation
Place Recognition of 3D Landmarks based on Geometric Relations
No full textPlace recognition based on landmarks or features is an important problem occurring in localization, mapping, computer vision and point cloud processing. In this paper, we present GLAROT-3D, a translation and rotation invariant 3D signature based on geometric relations. The proposed method encodes into a histogram the pairwise relative positions of keypoint features extracted from 3D sensor data. Since it relies only on geometric properties and not on specific feature descriptors, it does not require any prior training or vocabulary construction and enables lightweight comparisons between landmark maps. The similarity of two point maps is computed as the distance between the corresponding rotated histograms to achieve rotation invariance. Histogram rotation is enabled by efficient orientation histogram based on sphere cubical projection. The performance of GLAROT has been assessed through experiments with standard benchmark datasets
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