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 often called SLAM (simultaneous localization and mapping). 3D maps are essential to stay away from collisions with complicated hindrances and to self-localize in six levels of freedom
(x-, y-, z-position, roll, yaw and pitch angle). New ideas to the 6D SLAM challenge for 3D laser scans are proposed and a large choice of purposes are presented.

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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.

The depth is calculated only at the feature points and sparse range maps are created. 21). These cameras contain a CMOS sensor and additional laser diodes that illuminate the scene with modulated light. The CMOS sensor detects the light and computes on die the time-of-flight based on the detected phase shift. , the amount of light returned to the sensors (cf. 22). Similarly to a normal camera a 3D camera can be calibrated. Calibration determines the intrinsic parameters (the focal length (αx and αy ), the image center (u0 , v0 ) and the distortion coefficients (d1 , d2 ).

2). 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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