A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots

Zhou, Zhen; Wang, Dongqing; Xu, Boyang

doi:10.1108/aa-01-2020-0002

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…In order to improve the accuracy of the algorithm, the AEKF SLAM algorithm will be introduced after multi-innovation superposition, and the adjacent time information can be effectively utilized to make the filtering algorithm more accurate [21][22][23]. During the movement of the mobile robot, with the increase in the number of observed landmark points, the dimensions of system state vector keep increasing, which makes the calculation amount of covariance matrix and Jacobi matrix keep increasing.…”

Section: Introductionmentioning

confidence: 99%

Innovation-Superposed Simultaneous Localization and Mapping of Mobile Robots Based on Limited Augmentation

Yang

Song

et al. 2023

Electronics

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In this paper, Aaiming at the problem of simultaneous localization mapping (SLAM) for mobile robots, a limited-augmentation innovation superposition (LAIS) is proposed to solve the problems occurring in SLAM. By extending the single-time innovation superposition to multi-time innovation, the error accumulation during the movement of mobile robots is reduced and the accuracy of the algorithm is improved. At the same time, when the number of feature points observed by the sensor exceeds the threshold, the sensor range is restricted. Therefore, only the qualified feature points are added to the system state vector, which reduces the calculation amount of the algorithm and improves the running speed. Simulation results show that compared with other algorithms, LAIS has higher accuracy and higher running speed in environmental maps with a different number of landmark points.

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Section: Introductionmentioning

confidence: 99%

Innovation-Superposed Simultaneous Localization and Mapping of Mobile Robots Based on Limited Augmentation

Yang

Song

et al. 2023

Electronics

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“…In recently years, autonomous vehicles are widely used for achieving the transportation and manipulation tasks in industrial applications (Liu et al , 2020, 2016; Chen et al , 2019; Shamsfakhr and Bigham, 2020; Zhou et al , 2021). Localization is one of the basic functions for autonomous vehicles (Chen et al , 2019; Shamsfakhr and Bigham, 2020; Zhou et al , 2021), which plays a critical role in achieving the full autonomy.…”

Section: Introductionmentioning

confidence: 99%

Map-less long-term localization in complex industrial environments

Liu

Qiao

Suo

et al. 2021

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Purpose This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes. Design/methodology/approach The proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose. Findings Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach. Practical implications This paper provides comprehensive experiments in real industrial environments. Originality/value The system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.

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The modified multi-innovation adaptive EKF algorithm for identifying battery SOC

Sheng

Fan

et al. 2022

Ionics

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A multi-innovation with forgetting factor based EKF-SLAM method for mobile robots

Cited by 3 publications

References 31 publications

Innovation-Superposed Simultaneous Localization and Mapping of Mobile Robots Based on Limited Augmentation

Innovation-Superposed Simultaneous Localization and Mapping of Mobile Robots Based on Limited Augmentation

Map-less long-term localization in complex industrial environments

The modified multi-innovation adaptive EKF algorithm for identifying battery SOC

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