Line Segment Based Scan Matching for Concurrent Mapping and Localization of a Mobile Robot

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle's kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer P3ÀAT. The obtained results using smooth variable structure filter-simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter-simultaneous localization and mapping positions.

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“…In our work, we used the method ''Split and Merge'' which is largely used for line extraction. 24,25…”

Section: Inverse Observation Line-based Modelmentioning

confidence: 99%

“…There are many types of features association ''line to line,'' 24,25 ''point to line,'' 26 and ''point to point.'' 24,27 In this article, we will do a comparative's survey between two types of association ''point to point'' and ''line to line.''…”

Section: Inverse Observation Line-based Modelmentioning

confidence: 99%

Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

Demim

Nemra

Louadj

et al. 2018

Advances in Mechanical Engineering

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“…Scan matching techniques can be categorised based on their data association method such as point to point, point to feature , or feature to feature. In feature to feature matching, features such as line segments [13] and corners [11] are extracted from the 3D range data. In point to feature matching, points can be matched to line features, as done by [3].…”

Section: Localisation By Scan Matchingmentioning

confidence: 99%

Robotic Cybernavigation in Natural Known Environments

Jarvis

2010

2010 International Conference on Cyberworlds

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This paper concerns the navigation of a physical robot in real natural environments which have been previously scanned in considerable (3D and colour image) detail so as to permit virtual exploration by cybernavigation prior to mission replication in the real world. An on-board high speed 3D laser scanner is used to localize the robot (determine its position and orientation) in its working environment by applying scan matching against the model data previously collected.

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“…This method can be categorized into feature to feature, point to feature and point to point. In the feature to feature approaches, the properties of laser scanned data, such as line segments and corners, are extracted and then matched directly [15, 16]. This kind of approach requires the features' extraction from laser scanned data.…”

Section: Introductionmentioning

confidence: 99%

A Robotic Indoor 3D Mapping System Using a 2D Laser Range Finder Mounted on a Rotating Four-Bar Linkage of a Mobile Platform

Chou

Liu

2013

International Journal of Advanced Robotic Systems

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This paper describes our work in developing a 3D robotic mapping system composed by an experimental mobile platform equipped with a rotating laser range finder (LRF). For the purpose of obtaining more complete 3D scans of the environment, we design, construct and calibrate a crank‐rocker four‐bar linkage so that a LRF mounted on it could undergo repetitive rotational motion between two extreme positions, allowing both horizontal and vertical scans. To reduce the complexity of map representation suitable for optimization later, the local map from the LRF is a grid map represented by a distance‐transformed (DT) matrix. We compare the DT‐transformed maps and find the transformation matrix of a robot pose by a linear simplex‐based map optimization method restricted to a local region allows efficient alignment of maps in scan matching. Several indoor 2D and 3D mapping experiments are presented to demonstrate the consistency, efficiency and accuracy of the 3D mapping system for a mobile robot that is stationary or in motion

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Line Segment Based Scan Matching for Concurrent Mapping and Localization of a Mobile Robot

Cited by 11 publications

References 22 publications

Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

Robotic Cybernavigation in Natural Known Environments

A Robotic Indoor 3D Mapping System Using a 2D Laser Range Finder Mounted on a Rotating Four-Bar Linkage of a Mobile Platform

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