By Andreas Nüchter
The monograph written through Andreas Nüchter is concentrated on buying spatial types of actual environments via cellular robots. The robot mapping challenge is often known as SLAM (simultaneous localization and mapping). 3D maps are essential to steer clear of collisions with advanced stumbling blocks and to self-localize in six levels of freedom
(x-, y-, z-position, roll, yaw and pitch angle). New suggestions 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
Recently, diﬀerent groups employ rotating SICK scanners for acquiring 3D data [69, 117, 136]. Wulf et al. let the scanner rotate around the vertical axis. , gyros . In addition, their SLAM algorithms do not consider all 6 DoF. 3 Slice-Wise 6D SLAM Local 3D maps built by translated 2D laser scanners and 6D pose estimates are often used for mobile robot navigation. A well-known example is the grand challenge, where the Stanford racing team used this technique for high speed terrain classiﬁcation .
STAR 52, pp. 35–75. com © Springer-Verlag Berlin Heidelberg 2009 36 4 3D Range Image Registration Nm Nd ˆ i − (Rdˆj + t) wi,j m E(R, t) = 2 . 1) i=1 j=1 ˆ describes the same point in space as wi,j is assigned 1 if the i-th point of M ˆ Otherwise wi,j is 0. Two things have to be calculated: the j-th point of D. First, the corresponding points, and second, the transformation (R, t) that minimizes E(R, t) on the base of the corresponding points. The ICP algorithm calculates iteratively the point correspondences.
1 Planar 2D Mapping State of the art for planar 2D metric maps are probabilistic methods, where the robot has probabilistic motion and uncertain perception models. , Kalman or particle ﬁlter, it is possible to localize the robot. Mapping is often an extension to this A. : The Simultaneous Local. & Map. , STAR 52, pp. 29–33. 1 Overview of the dimensionality of SLAM approaches. Grey: 2D maps. Black: 3D maps. Sensor data Dimensionality of pose representation 3D 6D Planar 2D mapping Slice-wise 6D SLAM 2D mapping of planar sonar and 3D mapping using a prec ise 2D laser scans.
3D Robotic Mapping: The Simultaneous Localization and Mapping Problem with Six Degrees of Freedom by Andreas Nüchter