Novel Point-to-Point Scan Matching Algorithm Based on Cross-Correlation

Abstract

The localization of mobile robots in outdoor and indoor environments is a complex issue. Many sophisticated approaches, based on various types of sensory inputs and different computational concepts, are used to accomplish this task. However, many of the most efficient methods for mobile robot localization suffer from high computational costs and/or the need for high resolution sensory inputs. Scan cross-correlation is a traditional approach that can be, in special cases, used to match temporally aligned scans of robot environment. This work proposes a set of novel modifications to the cross-correlation method that extend its capability beyond these special cases to general scan matching and mitigate its computational costs so that it is usable in practical settings. The properties and validity of the proposed approach are in this study illustrated on a number of computational experiments.

Scan Matching by Cross-Correlation and Differential Evolution

“…This work proposes a novel scan matching algorithm based on a combination of two recently introduced approaches. The cross-correlation approach [42,43] is adopted for the assessment of laser range scans' similarity and the differential evolution is used for the search of accurate laser range scan transformation parameters [44]. The evaluation of the proposed scan matching strategy is performed in a series of simulation experiments with the help of a software framework introduced in [45].…”

“…In this study, the cross-corelation principle is used to match two laser range scans for indoor robot localization. Cross-corelation of a laser range scan, A, with a translated and rotated version of another scan, B, is maximized when the parameters of the transformation that correspond to the cumulative transition and rotation of the robot (scanner) between the scans were taken are found [42]. The evaluation of the cross-correlation between two 2-dimensional laser range scans under affine transformation T with parameter vector t = (t x , t y , Φ) can be expressed by…”

“…This fact can be used by the scan matching procedure. For example, each cell can have a weight corresponding to the number of points it contains [42]. The cell weight can then affect the remainder of the scan matching procedure.…”

“…A novel mobile robot self-localization strategy that uses the combination of cross-correlation of laser range scans [42] and the DE is designed and evaluated in this work. It improves a recent DE-based scan matching algorithm [44] through the use of laser range scan cross-correlation as an accurate and computationally lightweight fitness function.…”

Analysis of the use of genetic algorithms for indoor localisation via cloud point matching

“…The problem of line-of-sight indoor localisation was irst resolved through the matching of cloud point data (obtained from a line-ofsight sensor such as a Li-Dar) to retrieve tuple (x, y, θ ) describing the location and orientation of a robot in a known environment. This was irst achieved by algorithms such as the Iterative Closest Point (ICP) algorithm Besl and McKay [4], and a long line of alternative heuristic algorithms Lu and Milios [23], Diosi and Kleeman [11] [29], Biber and Strasser [5], Donoso-Aguirre et al [13], Konecny et al [19] and various improvements on the ICP's convergence speed [12] [32], dataset optimisation [33] [25] or precision metrics [14]. Performing indoor localisation without a priori knowledge of the robot's pose increases the diiculty to this problem, as a global search for the position must now be performed, rather than simply a pose reinement.…”