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1 – 10 of over 1000
Article
Publication date: 1 May 2019

Haoyao Chen, Hailin Huang, Ye Qin, Yanjie Li and Yunhui Liu

Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in…

Abstract

Purpose

Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements.

Design/methodology/approach

A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots.

Findings

The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system.

Originality/value

Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

Details

Assembly Automation, vol. 39 no. 2
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 19 June 2017

Michał R. Nowicki, Dominik Belter, Aleksander Kostusiak, Petr Cížek, Jan Faigl and Piotr Skrzypczyński

This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with…

Abstract

Purpose

This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics.

Design/methodology/approach

Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process.

Findings

The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory.

Research limitations/implications

The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research.

Practical implications

The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining.

Originality/value

The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.

Details

Industrial Robot: An International Journal, vol. 44 no. 4
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 25 February 2014

Yin-Tien Wang, Chen-Tung Chi and Ying-Chieh Feng

To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner…

194

Abstract

Purpose

To build a persistent map with visual landmarks is one of the most important steps for implementing the visual simultaneous localization and mapping (SLAM). The corner detector is a common method utilized to detect visual landmarks for constructing a map of the environment. However, due to the scale-variant characteristic of corner detection, extensive computational cost is needed to recover the scale and orientation of corner features in SLAM tasks. The purpose of this paper is to build the map using a local invariant feature detector, namely speeded-up robust features (SURF), to detect scale- and orientation-invariant features as well as provide a robust representation of visual landmarks for SLAM.

Design/methodology/approach

SURF are scale- and orientation-invariant features which have higher repeatability than that obtained by other detection methods. Furthermore, SURF algorithms have better processing speed than other scale-invariant detection method. The procedures of detection, description and matching of regular SURF algorithms are modified in this paper in order to provide a robust representation of visual landmarks in SLAM. The sparse representation is also used to describe the environmental map and to reduce the computational complexity in state estimation using extended Kalman filter (EKF). Furthermore, the effective procedures of data association and map management for SURF features in SLAM are also designed to improve the accuracy of robot state estimation.

Findings

Experimental works were carried out on an actual system with binocular vision sensors to prove the feasibility and effectiveness of the proposed algorithms. EKF SLAM with the modified SURF algorithms was applied in the experiments including the evaluation of accurate state estimation as well as the implementation of large-area SLAM. The performance of the modified SURF algorithms was compared with those obtained by regular SURF algorithms. The results show that the SURF with less-dimensional descriptors is the most suitable representation of visual landmarks. Meanwhile, the integrated system is successfully validated to fulfill the capabilities of visual SLAM system.

Originality/value

The contribution of this paper is the novel approach to overcome the problem of recovering the scale and orientation of visual landmarks in SLAM tasks. This research also extends the usability of local invariant feature detectors in SLAM tasks by utilizing its robust representation of visual landmarks. Furthermore, data association and map management designed for SURF-based mapping in this paper also give another perspective for improving the robustness of SLAM systems.

Details

Engineering Computations, vol. 31 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 28 December 2020

Zhen Zhou, Dongqing Wang and Boyang Xu

The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by…

Abstract

Purpose

The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity.

Design/methodology/approach

A multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations.

Findings

The simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping.

Originality/value

The method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.

Details

Assembly Automation, vol. 41 no. 1
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 8 January 2018

Jakob Mainert, Christoph Niepel, Thomas Lans and Samuel Greiff

This study aims at the employees’ view on organizational learning (OL). OL is originally assessed in the Strategic Learning Assessment Map (SLAM) at the level of the firm…

Abstract

Purpose

This study aims at the employees’ view on organizational learning (OL). OL is originally assessed in the Strategic Learning Assessment Map (SLAM) at the level of the firm by addressing managers, who rated OL in the SLAM on five dimensions of individual, group, organizational, feed-forward and feedback learning. However, as employees are getting their jobs done discretely and are increasingly making their own decisions, their perspective on OL genuinely matters. Hence, the authors assessed OL at the level of the individual by addressing employees on all levels, who rated OL in a short form of the SLAM (SF-SLAM).

Design/methodology/approach

In this paper, the authors focused on the construct validity of this SF-SLAM by investigating its reliability, factorial validity and nomological network. First, they asked whether the SF-SLAM reliably measures OL on five dimensions of individual, group, organizational, feed-forward and feedback learning. Next, they asked whether the SF-SLAM was associated with its nomological network of engaging in innovation-related learning activities, behaving innovatively on the job and showing higher educational levels, intelligence and individual job performances. They used a diverse German employee sample of skilled and unskilled workers and managers (N = 434) and analyzed the data with structural equation modeling.

Findings

The SF-SLAM was reliable, but revealed both constrained factorial validity and validity on the basis of its nomological network. First, five dimensions found support in the employee sample, but their correlations were high or very high, except for individual learning. Second, the SF-SLAM showed only few differential relations with variables from its nomological network.

Originality/value

Taken together, the SF-SLAM is short, reliable and only valid for examining individual learning.

Details

Journal of Knowledge Management, vol. 22 no. 1
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 11 June 2021

Ruihao Lin, Junzhe Xu and Jianhua Zhang

Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for…

Abstract

Purpose

Large-scale and precise three-dimensional (3D) map play an important role in autonomous driving and robot positioning. However, it is difficult to get accurate poses for mapping. On one hand, the global positioning system (GPS) data are not always reliable owing to multipath effect and poor satellite visibility in many urban environments. In another hand, the LiDAR-based odometry has accumulative errors. This paper aims to propose a novel simultaneous localization and mapping (SLAM) system to obtain large-scale and precise 3D map.

Design/methodology/approach

The proposed SLAM system optimally integrates the GPS data and a LiDAR odometry. In this system, two core algorithms are developed. To effectively verify reliability of the GPS data, VGL (the abbreviation of Verify GPS data with LiDAR data) algorithm is proposed and the points from LiDAR are used by the algorithm. To obtain accurate poses in GPS-denied areas, this paper proposes EG-LOAM algorithm, a LiDAR odometry with local optimization strategy to eliminate the accumulative errors by means of reliable GPS data.

Findings

On the KITTI data set and the customized outdoor data set, the system is able to generate high-precision 3D map in both GPS-denied areas and areas covered by GPS. Meanwhile, the VGL algorithm is proved to be able to verify reliability of the GPS data with confidence and the EG-LOAM outperform the state-of-the-art baselines.

Originality/value

A novel SLAM system is proposed to obtain large-scale and precise 3D map. To improve the robustness of the system, the VGL algorithm and the EG-LOAM are designed. The whole system as well as the two algorithms have a satisfactory performance in experiments.

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 12 June 2017

Chen-Chien Hsu, Cheng-Kai Yang, Yi-Hsing Chien, Yin-Tien Wang, Wei-Yen Wang and Chiang-Heng Chien

FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments…

Abstract

Purpose

FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms.

Design/methodology/approach

As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates.

Findings

Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy.

Originality/value

The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.

Details

Engineering Computations, vol. 34 no. 4
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 19 October 2018

Hui Xiong, Youping Chen, Xiaoping Li and Bing Chen

Because submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been…

157

Abstract

Purpose

Because submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.

Design/methodology/approach

Submaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.

Findings

Simulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.

Practical implications

The proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.

Originality/value

In this paper, a two-level optimized graph-based SLAM algorithm is proposed.

Details

Industrial Robot: An International Journal, vol. 45 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 17 October 2016

Hui Xiong, Youping Chen, Xiaoping Li, Bing Chen and Jun Zhang

The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It…

Abstract

Purpose

The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

Design/methodology/approach

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Findings

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

Originality/value

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.

Details

Industrial Robot: An International Journal, vol. 43 no. 6
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 13 November 2018

Guoqing Li, Yunhai Geng and Wenzheng Zhang

This paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission…

Abstract

Purpose

This paper aims to introduce an efficient active-simultaneous localization and mapping (SLAM) approach for rover navigation, future planetary rover exploration mission requires the rover to automatically localize itself with high accuracy.

Design/methodology/approach

A three-dimensional (3D) feature detection method is first proposed to extract salient features from the observed point cloud, after that, the salient features are employed as the candidate destinations for re-visiting under SLAM structure, followed by a path planning algorithm integrated with SLAM, wherein the path length and map utility are leveraged to reduce the growth rate of state estimation uncertainty.

Findings

The proposed approach is able to extract distinguishable 3D landmarks for feature re-visiting, and can be naturally integrated with any SLAM algorithms in an efficient manner to improve the navigation accuracy.

Originality/value

This paper proposes a novel active-SLAM structure for planetary rover exploration mission, the salient feature extraction method and active revisit patch planning method are validated to improve the accuracy of pose estimation.

Details

Aircraft Engineering and Aerospace Technology, vol. 91 no. 1
Type: Research Article
ISSN: 1748-8842

Keywords

1 – 10 of over 1000