Download 3D Robotic Mapping: The Simultaneous Localization and by Andreas Nüchter PDF

By Andreas Nüchter

ISBN-10: 3540898832

ISBN-13: 9783540898832

The monograph written by means of Andreas Nüchter is targeted on buying spatial types of actual environments via cellular robots. The robot mapping challenge is usually known as SLAM (simultaneous localization and mapping). 3D maps are essential to stay away from collisions with advanced stumbling blocks and to self-localize in six levels of freedom
(x-, y-, z-position, roll, yaw and pitch angle). New options to the 6D SLAM challenge for 3D laser scans are proposed and a wide selection of purposes are presented.

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Extra info for 3D Robotic Mapping: The Simultaneous Localization and Mapping Problem with Six Degrees of Freedom

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FastSLAM, that grows exponentially with the DoF, has been published to our knowledge. 5 Recent Trends A recent trend in SLAM research is to apply probabilistic methods to 3D mapping. Katz et al. use a probabilistic notion of ICP scan matching [67]. Weingarten et al. [133] and Cole et al. [19] use an extended Kalman filter on the mapping problem. In the present paper, we extend this state of the art by a GraphSLAM method. A similar approach was used in [127]. However, their algorithm is not practical due to the reported computational requirements.

The optimal displacement calculated by Eq. 31) corresponds to an affine motion. Therefore in a post processing step a rigid transformation (R, t) is calculated from (¯ c, c). 2 presents the displacement of a point using the affine transformation and rigid transformation. G xi xi + v(xi ) p·ϕ ϕ xi Fig. 2 The affine position of a 3D point xi + v(xi ) is different from the rigid transformation that results in point xi .. Based on [59]. The rigid transformation is calculated as follows: If c = 0 only a translation ¯.

All these methods show similar computational requirements (O(number of point pairs) with similar constants) and have all about the same accuracies depending on the resolution and similar stabilities with respect to noise data. The Minimal Number of Corresponding Points The closed form methods for computing the transformation (rotation R and translation t) for the ICP algorithm based on the corresponding points perform a low number of operations. Due to the orthonormality constraint the 9 entries of the rotation matrix contain only three free parameters: the rotation about the x-, y- and z- axis.

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