Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms

Eyvazpour, Reza; Shoaran, Maryam; Karimian, Ghader

doi:10.1007/s10462-022-10310-5

Cited by 10 publications

(4 citation statements)

References 214 publications

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“…In robotic applications, it is important for researchers to choose a SLAM algorithm that works well with the robot’s sensors. Integrating suitable hardware improves speed and performance in SLAM systems through accelerators, method optimization, and energy-efficient designs ( Eyvazpour et al, 2023 ). Various V-SLAM algorithms are designed for specific sensor types such as RGB-D, lidar, and stereo cameras.…”

Section: Guidelines For Evaluating and Selecting Visual Slam Methodsmentioning

confidence: 99%

A review of visual SLAM for robotics: evolution, properties, and future applications

Al-Tawil,

Hempel,

Abdelrahman

et al. 2024

Front. Robot. AI

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Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method’s workflow.

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Section: Guidelines For Evaluating and Selecting Visual Slam Methodsmentioning

confidence: 99%

A review of visual SLAM for robotics: evolution, properties, and future applications

Al-Tawil,

Hempel,

Abdelrahman

et al. 2024

Front. Robot. AI

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“…VO is a specific scenario within SfM and also the front-end part of SLAM algorithms [63]. It is the process of gradually estimating vehicle poses from a sequence of continuous images, facilitating the reconstruction of environmental information.…”

Section: Local Map Reconstructionmentioning

confidence: 99%

A Review of Crowdsourcing Update Methods for High-Definition Maps

Guo,

Zhou,

et al. 2024

IJGI

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High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

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“…Visual (inertial) odometry is becoming increasingly important in applications such as robotics, autonomous driving, and augmented reality [1][2][3]. The combination of a camera and an inertial measurement unit (IMU) is a popular and sensible choice, and Mourikis and Roumelioti [4] have shown that the tight integration of a visual and an inertial measurement unit can greatly improve the accuracy and robustness of an odometer.…”

Section: Introductionmentioning

confidence: 99%

Off-road environment Localization of unmanned vehicles based on Visual-Inertial

Firkat

Zhu

et al. 2023

Preprint

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Nowadays, simultaneous localization and mapping(SLAM), as one of the basic problems of autonomous driving technology, is used in more and more scenarios and fields, such as robot exploration, driverless cars, VR (virtual reality) ,and AR (augmented reality). However, most of the simultaneous localization and mapping studies in the past were conducted in a structured environment, and there was a lot of room for improvement in the performance in the complex environment in the field. In this paper, a visualization monocular visual-inertial odometry consisting of a monocular camera and an inertial measurement unit is introduced, and the front end of the system can operate robustly in the environment of large changes in field light, low texture and bumpy roads by fusing visual information and inertial measurement unit information at the front end, and using nonlinear optimization at the back end. The method in the paper can accurately measure six degrees of freedom (DOF) by combining only a monocular camera and an inertial measurement unit. We verified the performance of the algorithm on the public datasets ROOAD and EuRoC. The results are compared with other state-of-the-art algorithms to show that our method can indeed operate robustly in the field environment and surpass the best performing algorithms in EuRoC.

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Hardware implementation of SLAM algorithms: a survey on implementation approaches and platforms

Cited by 10 publications

References 214 publications

A review of visual SLAM for robotics: evolution, properties, and future applications

A review of visual SLAM for robotics: evolution, properties, and future applications

A Review of Crowdsourcing Update Methods for High-Definition Maps

Off-road environment Localization of unmanned vehicles based on Visual-Inertial

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