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

By Andreas Nüchter

The monograph written via Andreas Nüchter is concentrated on buying spatial versions of actual environments via cellular robots. The robot mapping challenge is usually known as SLAM (simultaneous localization and mapping). 3D maps are essential to steer clear of collisions with advanced hindrances 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 resources 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.

13) holds: tr RP ui uTi = tr uTi RT P ui = tr RT ui · P ui = RT ui · P ui . 42 4 3D Range Image Registration Since the vectors ui are unit vectors by construction and P and R are orthonormal matrices, we have RT ui · P ui ≤ 1. 3, Lemma 7). It follows, that tr (RP S) ≤ λ1 + λ2 + λ3 = tr (S) holds. According to this the maximum of tr (RP S) is reached, if RT P = ½ and accordingly R = P (cf. 3, Lemma 9 and Corollary 1). 12), is the rotation matrix and it holds: R = P = HS −1 = H(H T H)−1/2 . Again, the optimal translation is calculated as (cf.

Instead of storing a matrix W for the weights a vector of point correspondences is built. There is no influence to following computations; the amount of memory is usually reduced from O(n2 ) to O(n). Closed Form Solution Four algorithms are currently known that solve the error function of the ICP algorithm in closed form [76]. The difficulty of this minimization is to enforce the orthonormality constraint for the rotation matrix R. In the following the four algorithms are are presented. The first three algorithms separate the computation of the rotation R from the computation of the translation t.

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