The Algorithm of EKF-SLAM Using Laser Scanning System and Fisheye Camera

Kokovkina, Vera; Antipov, V. A.; Kirnos, Vasilii; Priorov, Andrey

doi:10.1109/synchroinfo.2019.8813985

Cited by 10 publications

(5 citation statements)

References 4 publications

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“…The filter-based method is a two-step iterative procedure derived from the Bayesian filter [11]. EKF SLAM is one of the popular filter approaches, which was firstly used in sonar sensors using the Extended Kalman Filter and can also be extended to LiDar and camera sensors [12,13]. Other similar Kalman series are the unscented Kalman filter and adaptive Kalman filter method [14].…”

Section: Mapping and Localizationmentioning

confidence: 99%

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Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information

Zhang

Wang

et al. 2022

Machines

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Localization is one of the most important issues in mobile robotics, especially when an autonomous mobile robot performs a navigation task. The current and popular occupancy grid map, based on 2D LiDar simultaneous localization and mapping (SLAM), is suitable and easy for path planning, and the adaptive Monte Carlo localization (AMCL) method can realize localization in most of the rooms in indoor environments. However, the conventional method fails to locate the robot when there are similar and repeated geometric structures, like long corridors. To solve this problem, we present Text-MCL, a new method for robot localization based on text information and laser scan data. A coarse-to-fine localization paradigm is used for localization: firstly, we find the coarse place for global localization by finding text-level semantic information, and then get the fine local localization using the Monte Carlo localization (MCL) method based on laser data. Extensive experiments demonstrate that our approach improves the global localization speed and success rate to 96.2% with few particles. In addition, the mobile robot using our proposed approach can recover from robot kidnapping after a short movement, while conventional MCL methods converge to the wrong position.

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Section: Mapping and Localizationmentioning

confidence: 99%

“…where the pose trajectory is firstly estimated by using odometry data and laser data. Given the estimated pose of the mobile robot, the SLAM task turns into a map construction with a known pose, which can be solved by using Equations ( 11)- (13).…”

Section: Map-building Using Text-level Informationmentioning

confidence: 99%

Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information

Zhang

Wang

et al. 2022

Machines

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“…Feature-based and direct approaches are used to solve V-SLAM. A feature-based filter is used with the help of the Kalman filter [68]; however, it has a high computational cost as it increases the state vector for a large environment. The loop closure problem can be solved proficiently with the help of a feature-based technique [69].…”

Section: Cameramentioning

confidence: 99%

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

et al. 2022

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Applications of mobile robots are continuously capturing the importance in numerous areas such as agriculture, surveillance, defense, and planetary exploration to name a few. Accurate navigation of a mobile robot is highly significant for its uninterrupted operation. Simultaneous localization and mapping (SLAM) is one of the widely used techniques in mobile robots for localization and navigation. SLAM consists of front- and back-end processes, wherein the front-end includes SLAM sensors. These sensors play a significant role in acquiring accurate environmental information for further processing and mapping. Therefore, understanding the operational limits of the available SLAM sensors and data collection techniques from a single sensor or multisensors is noteworthy. In this article, a detailed literature review of widely used SLAM sensors such as acoustic sensor, RADAR, camera, Light Detection and Ranging (LiDAR), and RGB-D is provided. The performance of SLAM sensors is compared using an analytical hierarchy process (AHP) based on various key indicators such as accuracy, range, cost, working environment, and computational cost.

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“…In [23], authors demonstrated the learning of a bias correction for the lidar motion estimate based on a Gaussian process. Authors in [24] [25], make use of the ROS [26] message filter to match different information sources up to some epsilon time difference.…”

Section: Related Workmentioning

confidence: 99%