Robust and Accurate Genetic Scan Matching Algorithm for Robotic Navigation

Lenac, Kristijan; Mumolo, Enzo; Nolich, Massimiliano

doi:10.1007/978-3-642-25486-4_58

Cited by 4 publications

(13 citation statements)

References 12 publications

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“…Further hybrid algorithms include a combination of GA⁄TrICP [22] which improved on previous GA⁄ICP algorithms [7] [24], and a rasterized GA ⁄ mbICP algorithm by Lenac et al [21].…”

Section: Existing Workmentioning

confidence: 99%

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

Boland

Indrusiak

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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A system's ability to precisely locate itself in a known physical environment is key to its capacity to interact with the environment in an intricate manner. The indoor localisation problem has been approached in a variety of ways, ranging from the identiication of pre-deined features or topologies to the more general cloud-point matching.Cloud point matching can be achieved using a variety of algorithms, each with beneits and drawbacks. Recent improvements have focused on the application of genetic algorithms to solve the initial 'global' search for a solution, before reining this solution to a precise position through a non-genetic algorithm. This project aims to demonstrate the ineiciency of genetic algorithms applied to the global search problem for the issue of indoor localisation; this is thought to be caused by the solution space's low dimensionality, solution landscape topology and the ineicacy of crossover operators in the algorithm. Based on our assumptions of map topologies, we conclude that signiicant redundancies can be found in some purely genetic heuristics and suggest further development of landscape analysis to allow the use of algorithms appropriate to the scenario's complexity.

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“…Further hybrid algorithms include a combination of GA⁄TrICP [22] which improved on previous GA⁄ICP algorithms [7] [24], and a rasterized GA ⁄ mbICP algorithm by Lenac et al [21].…”

Section: Existing Workmentioning

confidence: 99%

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

Boland

Indrusiak

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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“…Previous genetic scan matching algorithms, such as the Hybrid Genetic Scan Matcher [25], propose to use a genetic search using a correlation based metric followed by a round of ICP to refine the solution. We propose two extensions to this algorithm.…”

Section: Right Geometry Scannermentioning

confidence: 99%

“…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.…”

Section: Loop Closure Transform Estimationmentioning

confidence: 99%

“…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 .…”

Section: Loop Closure Transform Estimationmentioning

confidence: 99%

“…In particular, we use the library's FICP implementation as it provided the best performance on our data. Secondly, we compare our proposed FGSM algorithm with the original hybrid genetic algorithm from which it was extended [25].…”

Section: Fgsm Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

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

Corso

Zakhor

2013

Remote Sensing

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Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center.

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Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments

Cuzzocrea

Lenac

Mumolo

2021

Lecture Notes in Computer Science

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Big moving objects arise as a novel class of big data objects in emerging environments. Here, the main problems are the following: (i) tracking, which represents the baseline operation for a plethora of higherlevel functionalities, such as detection, classification, and so forth; (ii) analysis, which meaningfully marries with big data analytics scenarios. In line with these goals, in this paper we propose a novel family of scan matching algorithms based on registration, which are enhanced by using a genetic pre-alignment phase based on a novel metrics, fist, and, second, performing a finer alignment using a deterministic approach. Our experimental assessment and analysis confirms the benefits deriving from the proposed novel family of such algorithms.

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Robust and Accurate Genetic Scan Matching Algorithm for Robotic Navigation

Cited by 4 publications

References 12 publications

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

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

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

Enhancing Scan Matching Algorithms via Genetic Programming for Supporting Big Moving Objects Tracking and Analysis in Emerging Environments

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