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1 – 10 of 414
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: 14 June 2013

Edgardo Molina, Alpha Diallo and Zhigang Zhu

The purpose of this paper is to propose a local orientation and navigation framework based on visual features that provide location recognition, context augmentation, and…

Abstract

Propose

The purpose of this paper is to propose a local orientation and navigation framework based on visual features that provide location recognition, context augmentation, and viewer localization information to a blind or low‐vision user.

Design/methodology/approach

The authors consider three types of “visual noun” features: signage, visual‐text, and visual‐icons that are proposed as a low‐cost method for augmenting environments. These are used in combination with an RGB‐D sensor and a simplified SLAM algorithm to develop a framework for navigation assistance suitable for the blind and low‐vision users.

Findings

It was found that signage detection cannot only help a blind user to find a location, but can also be used to give accurate orientation and location information to guide the user navigating a complex environment. The combination of visual nouns for orientation and RGB‐D sensing for traversable path finding can be one of the cost‐effective solutions for navigation assistance for blind and low‐vision users.

Research limitations/implications

This is the first step for a new approach in self‐localization and local navigation of a blind user using both signs and 3D data. The approach is meant to be cost‐effective but it only works in man‐made scenes where a lot of signs exist or can be placed and are relatively permanent in their appearances and locations.

Social implications

Based on 2012 World Health Organization, 285 million people are visually impaired, of which 39 million are blind. This project will have a direct impact on this community.

Originality/value

Signage detection has been widely studied for assisting visually impaired people in finding locations, but this paper provides the first attempt to use visual nouns as visual features to accurately locate and orient a blind user. The combination of visual nouns with 3D data from an RGB‐D sensor is also new.

Details

Journal of Assistive Technologies, vol. 7 no. 2
Type: Research Article
ISSN: 1754-9450

Keywords

Article
Publication date: 27 May 2020

Quentin Kevin Gautier, Thomas G. Garrison, Ferrill Rushton, Nicholas Bouck, Eric Lo, Peter Tueller, Curt Schurgers and Ryan Kastner

Digital documentation techniques of tunneling excavations at archaeological sites are becoming more common. These methods, such as photogrammetry and LiDAR (Light…

Abstract

Purpose

Digital documentation techniques of tunneling excavations at archaeological sites are becoming more common. These methods, such as photogrammetry and LiDAR (Light Detection and Ranging), are able to create precise three-dimensional models of excavations to complement traditional forms of documentation with millimeter to centimeter accuracy. However, these techniques require either expensive pieces of equipment or a long processing time that can be prohibitive during short field seasons in remote areas. This article aims to determine the effectiveness of various low-cost sensors and real-time algorithms to create digital scans of archaeological excavations.

Design/methodology/approach

The authors used a class of algorithms called SLAM (Simultaneous Localization and Mapping) along with depth-sensing cameras. While these algorithms have largely improved over recent years, the accuracy of the results still depends on the scanning conditions. The authors developed a prototype of a scanning device and collected 3D data at a Maya archaeological site and refined the instrument in a system of natural caves. This article presents an analysis of the resulting 3D models to determine the effectiveness of the various sensors and algorithms employed.

Findings

While not as accurate as commercial LiDAR systems, the prototype presented, employing a time-of-flight depth sensor and using a feature-based SLAM algorithm, is a rapid and effective way to document archaeological contexts at a fraction of the cost.

Practical implications

The proposed system is easy to deploy, provides real-time results and would be particularly useful in salvage operations as well as in high-risk areas where cultural heritage is threatened.

Originality/value

This article compares many different low-cost scanning solutions for underground excavations, along with presenting a prototype that can be easily replicated for documentation purposes.

Details

Journal of Cultural Heritage Management and Sustainable Development, vol. 10 no. 4
Type: Research Article
ISSN: 2044-1266

Keywords

Article
Publication date: 1 April 2003

Paolo Pirjanian, Niklas Karlsson, Luis Goncalves and Enrico Di Bernardo

One difficult problem in robotics is localization: the ability of a mobile robot to determine its position in the environment. Roboticists around the globe have been…

Abstract

One difficult problem in robotics is localization: the ability of a mobile robot to determine its position in the environment. Roboticists around the globe have been working to find a solution to localization for more than 20 years; however, only in the past 4‐5 years we have seen some promising results. In this work, we describe a first‐of‐a‐kind, breakthrough technology for localization that requires only one low‐cost camera (less than 50USD) and odometry to provide localization. Because of its low‐cost and robust performance in realistic environments, this technology is particularly well‐suited for use in consumer and commercial applications.

Details

Industrial Robot: An International Journal, vol. 30 no. 2
Type: Research Article
ISSN: 0143-991X

Keywords

Article
Publication date: 8 July 2022

Lin Zhang and Yingjie Zhang

This paper aims to quickly obtain an accurate and complete dense three-dimensional map of indoor environment with lower cost, which can be directly used in navigation.

Abstract

Purpose

This paper aims to quickly obtain an accurate and complete dense three-dimensional map of indoor environment with lower cost, which can be directly used in navigation.

Design/methodology/approach

This paper proposes an improved ORB-SLAM2 dense map optimization algorithm. This algorithm consists of three parts: ORB feature extraction based on improved FAST-12, feature point extraction with progressive sample consensus (PROSAC) and the dense ORB-SLAM2 algorithm for mapping. Here, the dense ORB-SLAM2 algorithm adds LoopClose optimization thread and dense point cloud map and octree map construction thread. The dense map is computationally expensive and occupies a large amount of memory. Therefore, the proposed method takes higher efficiency, voxel filtering can reduce the memory while ensuring the density of the map and then use the octree format to store the map to further reduce memory.

Findings

The improved ORB-SLAM2 algorithm is compared with the original ORB-SLAM2 algorithm, and the experimental results show that the map through improved ORB-SLAM2 can be directly used in navigation process with higher accuracy, shorter tracking time and smaller memory.

Originality/value

The improved ORB-SLAM2 algorithm can obtain a dense environment map, which ensures the integrity of data. The comparisons of FAST-12 and improved FAST-12, RANSAC and PROSAC prove that the improved FAST-12 and PROSAC both make the feature point extraction process faster and more accurate. Voxel filter helps to take small storage memory and low computation cost, and octree map construction on the dense map can be directly used in navigation.

Details

Assembly Automation, vol. 42 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

Article
Publication date: 14 June 2013

Christian Ivancsits and Min‐Fan Ricky Lee

This paper aims to address three major issues in the development of a vision‐based navigation system for small unmanned aerial vehicles (UAVs) which can be characterized…

1013

Abstract

Purpose

This paper aims to address three major issues in the development of a vision‐based navigation system for small unmanned aerial vehicles (UAVs) which can be characterized as follows: technical constraints, robust image feature matching and an efficient and precise method for visual navigation.

Design/methodology/approach

The authors present and evaluate methods for their solution such as wireless networked control, highly distinctive feature descriptors (HDF) and a visual odometry system.

Findings

Proposed feature descriptors achieve significant improvements in computation time by detaching the explicit scale invariance of the widely used scale invariant feature transform. The feasibility of wireless networked real‐time control for vision‐based navigation is evaluated in terms of latency and data throughput. The visual odometry system uses a single camera to reconstruct the camera path and the structure of the environment, and achieved and error of 1.65 percent w.r.t total path length on a circular trajectory of 9.43 m.

Originality/value

The originality/value lies in the contribution of the presented work to the solution of visual odometry for small unmanned aerial vehicles.

Article
Publication date: 9 October 2019

Rokas Jurevičius and Virginijus Marcinkevičius

The purpose of this paper is to present a new data set of aerial imagery from robotics simulator (AIR). AIR data set aims to provide a starting point for localization…

Abstract

Purpose

The purpose of this paper is to present a new data set of aerial imagery from robotics simulator (AIR). AIR data set aims to provide a starting point for localization system development and to become a typical benchmark for accuracy comparison of map-based localization algorithms, visual odometry and SLAM for high-altitude flights.

Design/methodology/approach

The presented data set contains over 100,000 aerial images captured from Gazebo robotics simulator using orthophoto maps as a ground plane. Flights with three different trajectories are performed on maps from urban and forest environment at different altitudes, totaling over 33 kilometers of flight distance.

Findings

The review of previous research studies show that the presented data set is the largest currently available public data set with downward facing camera imagery.

Originality/value

This paper presents the problem of missing publicly available data sets for high-altitude (100‒3,000 meters) UAV flights; the current state-of-the-art research studies performed to develop map-based localization system for UAVs depend on real-life test flights and custom-simulated data sets for accuracy evaluation of the algorithms. The presented new data set solves this problem and aims to help the researchers to improve and benchmark new algorithms for high-altitude flights.

Details

International Journal of Intelligent Unmanned Systems, vol. 8 no. 3
Type: Research Article
ISSN: 2049-6427

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: 13 May 2014

Tianmiao Wang, Chaolei Wang, Jianhong Liang and Yicheng Zhang

The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned…

Abstract

Purpose

The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs).

Design/methodology/approach

Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management.

Findings

Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system.

Originality/value

The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

Details

Industrial Robot: An International Journal, vol. 41 no. 3
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

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