Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques

Malagon-Soldara, Salvador M.; Toledano‐Ayala, Manuel; Soto-Zarazúa, Genaro M.; Carrillo-Serrano, Roberto V.; Rivas-Araiza, Edgar A.

doi:10.11591/ijra.v4i1.pp73-81

Cited by 12 publications

(5 citation statements)

References 38 publications

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“…SLAM algorithm that has been presently researched such as the Kalman filter algorithm can only be applied to the non-linear Gaussian distribution model, which can cause high computational time, linearization error and which can lead to non-accurate estimation. In addition, according to [10], extended Kalman filter algorithm can lead to poor representations of nonlinear function. This can cause the filter to be divergent.…”

Section: Introductionmentioning

confidence: 99%

Visual analytics of 3D LiDAR point clouds in robotics operating systems

Azri¹,

Abdul-Rahman²,

Hamzah³

et al. 2020

Bulletin EEI

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This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on Simultaneous Localization and Mapping (SLAM) using point cloud data derived from the Light Detection and Ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as Grid Mapping and Monte Carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on Real-Time Appearance-Based (RTAB) and makes use of the Red Green Blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the Robot Operating System (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition, the probability for the estimated location is improved which allows for better vehicle maneuverability.

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Section: Introductionmentioning

confidence: 99%

Visual analytics of 3D LiDAR point clouds in robotics operating systems

Azri¹,

Abdul-Rahman²,

Hamzah³

et al. 2020

Bulletin EEI

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“…Probabilistic methods are differing since they are explicitly identifying probabilities with possible robot positions. Therefore, the recent study is focusing on probabilistic localization approaches [26]. A set of methodologies have been proposed as follows: kalman filter localization, markov localization, Monte Carlo localization, route-based localization, positioning beacon systems, globally unique localization, landmark-based navigation, the stochastic map technique, autonomous map building, dynamic environments, and cyclic environments.…”

Section: Map Representationmentioning

confidence: 99%

“…A set of methodologies have been proposed as follows: kalman filter localization, markov localization, Monte Carlo localization, route-based localization, positioning beacon systems, globally unique localization, landmark-based navigation, the stochastic map technique, autonomous map building, dynamic environments, and cyclic environments. The techniques and algorithms were thoroughly examined are proposed in [6], [24]- [26].…”

Section: Map Representationmentioning

confidence: 99%

A one decade survey of autonomous mobile robot systems

Zghair

Al-Araji

2021

IJECE

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<span lang="EN-US">Recently, autonomous mobile robots have gained popularity in the modern world due to their relevance technology and application in real world situations. The global market for mobile robots will grow significantly over the next 20 years. Autonomous mobile robots are found in many fields including institutions, industry, business, hospitals, agriculture as well as private households for the purpose of improving day-to-day activities and services. The development of technology has increased in the requirements for mobile robots because of the services and tasks provided by them, like rescue and research operations, surveillance, carry heavy objects and so on. Researchers have conducted many works on the importance of robots, their uses, and problems. This article aims to analyze the control system of mobile robots and the way robots have the ability of moving in real-world to achieve their goals. It should be noted that there are several technological directions in a mobile robot industry. It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems. All such systems should be united through a control unit; thus, the mission or work of mobile robots are conducted with reliability.</span>

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“…The SLAM localization is essential for the navigation of a robot to be autonomous, requiring robust calculations and approximations to determine the robot's position in the environment, in order to know how to act during its movement for the reliable execution of tasks [12]. However, the localization is subject to many factors that contribute to its lack of accuracy, e.g., encoder/odometry and mapping errors, location uncertainty based on scan matching, loss of previous position information, sensor inaccuracy and environmental conditions.…”

Section: A Simultaneous Localization and Mappingmentioning

confidence: 99%

Improving the Mobile Robots Indoor Localization System by Combining SLAM with Fiducial Markers

Oliveira

Piardi

Bertogna

et al. 2021

2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Edu

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Autonomous mobile robots applications require a robust navigation system, which ensures the proper movement of the robot while performing their tasks. The key challenge in the navigation system is related to the indoor localization. Simultaneous Localization and Mapping (SLAM) techniques combined with Adaptive Monte Carlo Localization (AMCL) are widely used to localize robots. However, this approach is susceptible to errors, especially in dynamic environments and in presence of obstacles and objects. This paper presents an approach to improve the estimation of the indoor pose of a wheeled mobile robot in an environment. To this end, the proposed localization system integrates the AMCL algorithm with the position updates and corrections based on the artificial vision detection of fiducial markers scattered throughout the environment to reduce the errors accumulated by the AMCL position estimation. The proposed approach is based on Robot Operating System (ROS), and tested and validated in a simulation environment. As a result, an improvement in the trajectory performed by the robot was identified using the SLAM system combined with traditional AMCL corrected with the detection, by artificial vision, of fiducial markers. I. INTRODUCTIONMobile robots are widespread in several areas, such as industrial automation, agriculture, medical care, autonomous driving, product deliveries, planetary exploration, smart warehouses, personal services, construction, reconnaissance, entertainment, emergency rescue operations, patrolling and transportation [1]. Being one of the fastest-growing scientific fields today, mobile robotics has considerable impact not only on research but also on the economy. Even with the Covid-19 pandemic scenario, the robotics market is expected to move around U$23 billion in 2021 and growing to U$54 billion in 2023 [2], pointing to a considerable expansion of autonomous mobile robots applications.Characterized as intelligent systems, mobile robots have the ability to move autonomously, without the human interference [3], making decisions based on the information collected from their sensors (e.g., LiDAR, sonar and cameras), which allows them to help humans with heavy or timeconsuming tasks [4]. However, for a mobile robot to be able

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Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques

Cited by 12 publications

References 38 publications

Visual analytics of 3D LiDAR point clouds in robotics operating systems

Visual analytics of 3D LiDAR point clouds in robotics operating systems

A one decade survey of autonomous mobile robot systems

Improving the Mobile Robots Indoor Localization System by Combining SLAM with Fiducial Markers

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