Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms

Abstract

The paper reports on mobile robot motion estimation based on matching points from successive two-dimensional ͑2D͒ laser scans. This ego-motion approach is well suited to unstructured and dynamic environments because it directly uses raw laser points rather than extracted features. We have analyzed the application of two methods that are very different in essence: ͑i͒ A 2D version of iterative closest point ͑ICP͒, which is widely used for surface registration; ͑ii͒ a genetic algorithm ͑GA͒, which is a novel approach for this kind of problem. Their performance in terms of real-time applicability and accuracy has been compared in outdoor experiments with nonstop motion under diverse realistic navigation conditions. Based on this analysis, we propose a hybrid GA-ICP algorithm that combines the best characteristics of these pure methods. The experiments have been carried out with the tracked mobile robot Auriga-␣ and an on-board 2D laser scanner.

Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems

“…One of the techniques that is commonly adopted in LiDAR-based motion estimation is scan matching. The scan matching methods can be broadly classified into three different categories: feature-based scan matching, point-based scan matching and mathematical property-based scan matching [5]. Feature-based scan matching extracts distinctive geometrical patterns, such as line segments [6][7][8][9], corners and jump edges [10,11], lane makers [12] and curvature functions [13], from the LiDAR measurements.…”

Motion Detection from Mobile Robots with Fuzzy Threshold Selection in Consecutive 2D Laser Scans

“…The range scan at discrete time k-1 is a set of Cartesian coordinates , indexed by j = 1 to N. The current scan at time k , is projected into the previous XY frame to obtain , according to odometric data, where J(j) represents the correspondence index function [23]. Note that with laser scanners with a limited field of view there can be points without correspondence.…”

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

“…Similar to the previous works of Martinez et al [38] and Lenac et al [25], we parameterize the solution space using a three element chromosome. Specifically, each individual in the population consists of two translational and one rotational parameter.…”

“…In contrast to the existing approaches of [25,38], we propose a new algorithm, Fractional Genetic Scan Matching (FGSM), which introduces a transformation function into the lifetime of each chromosome. Specifically, FGSM starts by considering a random set of chromosomes, S 0 , sampled uniformly from a rough initial condition, µ 0 .…”