Metric-based iterative closest point scan matching for sensor displacement estimation

Abstract

Fig. 10. Distance (7) versus initial orientation (left) of robot R from the segment P P (right). [Fig. 10 (right)]; as expected, while the robot turns, the distance is smooth almost everywhere [Fig. 10 (left)]. Values for the robot and the segment are R1 : l1 = 0:28 1 = =6 R 2 : l 2 = 0:2 2 = 3=4 R 3 : l 3 = 0:2 3 = 5=4 R 4 : l 4 = 0:28 4 = 11=6 P1 : (00:3; 0:4) P 2 : (0:3; 0:4): VII. CONCLUSIONIn this paper, we presented an analytical method to compute the nonholonomic distance of a polygonal RS car from a polygonal obstacle. By extending the original RS work, we computed the shortest distance to a manifold (the C-obstacle), rather than to a point. In particular, we were able to reduce this problem to that of finding the solution of a set of algebraic equations by using geometric and optimal control arguments, which also provided deeper understanding of the underlying structure of the shortest paths. Moreover, the distance d() being a piecewise smooth function of the robot state , it is easy to compute analytically the gradient 0 ! r d() almost everywhere; thus, it is possible to build an artificial potential field with d(). The computation of a candidate optimal path is performed in constant time. For a robot and an environment with m and n vertices, respectively, the complexity is O(3 3 m 3 n 3 26), where 3 accounts for the three subproblems that must be solved to compute the distance, and 26 accounts for the number of candidate paths whose length will determine the distance value. REFERENCES[1] J. Latombe, Robot Motion Planning. Boston, MA: Kluwer, 1991.[2] J.-P. Laumond and P. Souères, "Metric induced by the shortest paths for a car-like mobile robot," in Proc. Int. Conf. Intell. Robots Syst., 1993, pp. 1299-1303 [3] L. Dubins, "On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal positions and tangents," Amer.Abstract-This paper addresses the scan matching problem for mobile robot displacement estimation. The contribution is a new metric distance and all the tools necessary to be used within the iterative closest point framework. The metric distance is defined in the configuration space of the sensor, and takes into account both translation and rotation error of the sensor. The new scan matching technique ameliorates previous methods in terms of robustness, precision, convergence, and computational load. Furthermore, it has been extensively tested to validate and compare this technique with existing methods.Index Terms-Mobile robots, scan matching, sensor displacement estimation.

Standalone Terrestrial Laser Scanning for Efficiently Capturing Aec Buildings for as-Built Bim

“…The fine registration usually is performed using some variant of the iterative closest point algorithm (ICP) (Besl and McKay, 1992, Chetverikov et al, 2002, Minguez et al, 2006. Given a good initial alignment, these algorithms are able to converge to an optimal solution.…”

Data association for a hybrid metric map representation

“…For each potential DA hypothesis [f i , g j ], the error occurred in g j 's extraction process is calibrated by iterative closest point (ICP) algorithm [18].…”

“…Two indexes are adopted to represent the incompatibility. They are the shadowed area R u (17) and shadowed angle α u : (18) where * u1 and * u2 are calculated by treating currently observation Z 0 and saved history observation z s i .Z l as exploring free area of the environment respectively.…”

Modeling dynamic scenarios for local sensor-based motion planning

“…Many algorithms use a scan matching technique to correct the robot position and incrementally build a local map [5,26,7,32]. The performance of these techniques is affected by dynamic environments due to failures in the matching process.…”