scholarly journals GPS-SLAM: An Augmentation of the ORB-SLAM Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4973 ◽  
Author(s):  
Dániel Kiss-Illés ◽  
Cristina Barrado ◽  
Esther Salamí
Keyword(s):  
Frame Rate ◽  
Measurement Unit ◽  
Additional Information ◽  
Main Work ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Aerial Vehicle ◽  
Slam Algorithm ◽  
Low Frame Rate

This work presents Global Positioning System-Simultaneous Localization and Mapping (GPS-SLAM), an augmented version of Oriented FAST (Features from accelerated segment test) and Rotated BRIEF (Binary Robust Independent Elementary Features) feature detector (ORB)-SLAM using GPS and inertial data to make the algorithm capable of dealing with low frame rate datasets. In general, SLAM systems are successful in case of datasets with a high frame rate. This work was motivated by a scarce dataset where ORB-SLAM often loses track because of the lack of continuity. The main work includes the determination of the next frame’s pose based on the GPS and inertial data. The results show that this additional information makes the algorithm more robust. As many large, outdoor unmanned aerial vehicle (UAV) flights save the GPS and inertial measurement unit (IMU) data of the capturing of images, this program gives an option to use the SLAM algorithm successfully even if the dataset has a low frame rate.

scholarly journals An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2004 ◽  
Author(s):  
Linlin Xia ◽  
Qingyu Meng ◽  
Deru Chi ◽  
Bo Meng ◽  
Hanrui Yang
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Feature Matching ◽  
Robot Navigation ◽  
Measurement Unit ◽  
State Estimator ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Tightly Coupled ◽  
Association State

The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.

Visual-inertial SLAM method based on optical flow in a GPS-denied environment

2018 ◽  
Vol 45 (3) ◽  
pp. 401-406 ◽  
Author(s):  
Chang Chen ◽  
Hua Zhu
Keyword(s):  
Optical Flow ◽  
Measurement Unit ◽  
Data Sets ◽  
Theoretical Derivation ◽  
Content Type ◽  
Feature Extraction Method ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Tightly Coupled ◽  
The Stability

Purpose This study aims to present a visual-inertial simultaneous localization and mapping (SLAM) method for accurate positioning and navigation of mobile robots in the event of global positioning system (GPS) signal failure in buildings, trees and other obstacles. Design/methodology/approach In this framework, a feature extraction method distributes features on the image under texture-less scenes. The assumption of constant luminosity is improved, and the features are tracked by the optical flow to enhance the stability of the system. The camera data and inertial measurement unit data are tightly coupled to estimate the pose by nonlinear optimization. Findings The method is successfully performed on the mobile robot and steadily extracts the features on low texture environments and tracks features. The end-to-end error is 1.375 m with respect to the total length of 762 m. The authors achieve better relative pose error, scale and CPU load than ORB-SLAM2 on EuRoC data sets. Originality/value The main contribution of this study is the theoretical derivation and experimental application of a new visual-inertial SLAM method that has excellent accuracy and stability on weak texture scenes.

Rao-Blackwellized visual SLAM for small UAVs with vehicle model partition

2014 ◽  
Vol 41 (3) ◽  
pp. 266-274 ◽  
Author(s):  
Tianmiao Wang ◽  
Chaolei Wang ◽  
Jianhong Liang ◽  
Yicheng Zhang
Keyword(s):  
Particle Filter ◽  
Internal State ◽  
Factorization Method ◽  
Measurement Unit ◽  
Visual Slam ◽  
Vehicle Model ◽  
Theoretical Derivation ◽  
Content Type ◽  
Localization And Mapping ◽  
Slam Algorithm

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.

scholarly journals An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment

2021 ◽  
Vol 11 (4) ◽  
pp. 1828
Author(s):  
Yakun Wu ◽  
Li Luo ◽  
Shujuan Yin ◽  
Mengqi Yu ◽  
Fei Qiao ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Mobile Robots ◽  
Semantic Information ◽  
Dynamic Environment ◽  
High Energy ◽  
Significant Loss ◽  
Frame Rate ◽  
Energy Efficiency Improvement ◽  
Localization And Mapping ◽  
Slam Algorithm

The Simultaneous Localization and Mapping (SLAM) algorithm is a hotspot in robot application research with the ability to help mobile robots solve the most fundamental problems of “localization” and “mapping”. The visual semantic SLAM algorithm fused with semantic information enables robots to understand the surrounding environment better, thus dealing with complexity and variability of real application scenarios. DS-SLAM (Semantic SLAM towards Dynamic Environment), one of the representative works in visual semantic SLAM, enhances the robustness in the dynamic scene through semantic information. However, the introduction of deep learning increases the complexity of the system, which makes it a considerable challenge to achieve the real-time semantic SLAM system on the low-power embedded platform. In this paper, we realized the high energy-efficiency DS-SLAM algorithm on the Field Programmable Gate Array (FPGA) based heterogeneous platform through the optimization co-design of software and hardware with the help of OpenCL (Open Computing Language) development flow. Compared with Intel i7 CPU on the TUM dataset, our accelerator achieves up to 13× frame rate improvement, and up to 18× energy efficiency improvement, without significant loss in accuracy.

3D Terrain Reconstruction by Small Unmanned Aerial Vehicle Using SIFT-Based Monocular SLAM

2011 ◽  
Vol 23 (2) ◽  
pp. 292-301 ◽  
Author(s):  
Taro Suzuki ◽  
◽  
Yoshiharu Amano ◽  
Takumi Hashizume ◽  
Shinji Suzuki ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Effective Means ◽  
3D Measurement ◽  
Scale Invariant ◽  
Practical Applications ◽  
Monocular Camera ◽  
3D Terrain ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Slam Algorithm

This paper describes a Simultaneous Localization And Mapping (SLAM) algorithm using a monocular camera for a small Unmanned Aerial Vehicle (UAV). A small UAV has attracted the attention for effective means of the collecting aerial information. However, there are few practical applications due to its small payloads for the 3D measurement. We propose extended Kalman filter SLAM to increase UAV position and attitude data and to construct 3D terrain maps using a small monocular camera. We propose 3D measurement based on Scale-Invariant Feature Transform (SIFT) triangulation features extracted from captured images. Field-experiment results show that our proposal effectively estimates position and attitude of the UAV and construct the 3D terrain map.

scholarly journals Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation in Orchards

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 339
Author(s):  
Kai Wang ◽  
Jun Zhou ◽  
Wenhai Zhang ◽  
Baohua Zhang
Keyword(s):  
Point Cloud ◽  
Search Algorithm ◽  
Clustering Algorithms ◽  
Fruit Trees ◽  
Measurement Unit ◽  
Scanning System ◽  
Localization And Mapping ◽  
Intelligent Measurement ◽  
Canopy Volume ◽  
Slam Algorithm

To meet the demand for canopy morphological parameter measurements in orchards, a mobile scanning system is designed based on the 3D Simultaneous Localization and Mapping (SLAM) algorithm. The system uses a lightweight LiDAR-Inertial Measurement Unit (LiDAR-IMU) state estimator and a rotation-constrained optimization algorithm to reconstruct a point cloud map of the orchard. Then, Statistical Outlier Removal (SOR) filtering and European clustering algorithms are used to segment the orchard point cloud from which the ground information has been separated, and the k-nearest neighbour (KNN) search algorithm is used to restore the filtered point cloud. Finally, the height of the fruit trees and the volume of the canopy are obtained by the point cloud statistical method and the 3D alpha-shape algorithm. To verify the algorithm, tracked robots equipped with LIDAR and an IMU are used in a standardized orchard. Experiments show that the system in this paper can reconstruct the orchard point cloud environment with high accuracy and can obtain the point cloud information of all fruit trees in the orchard environment. The accuracy of point cloud-based segmentation of fruit trees in the orchard is 95.4%. The R2 and Root Mean Square Error (RMSE) values of crown height are 0.93682 and 0.04337, respectively, and the corresponding values of canopy volume are 0.8406 and 1.5738, respectively. In summary, this system achieves a good evaluation result of orchard crown information and has important application value in the intelligent measurement of fruit trees.

scholarly journals Simulation and Analysis of Particle Filter Based Slam System

2018 ◽  
Vol 25 (1) ◽  
pp. 137-153
Author(s):  
Piotr Kaniewski ◽  
Paweł Słowak
Keyword(s):  
Particle Filter ◽  
Unmanned Aerial Vehicle ◽  
Simultaneous Localization And Mapping ◽  
Flight Trajectory ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Slam Algorithm

AbstractThe paper describes a problem and an algorithm for simultaneous localization and mapping (SLAM) for an unmanned aerial vehicle (UAV). The algorithm developed by the authors estimates the flight trajectory and builds a map of the terrain below the UAV. As a tool for estimating the UAV position and other parameters of flight, a particle filter was applied. The proposed algorithm was tested and analyzed by simulations and the paper presents a simulator developed by the authors and used for SLAM testing purposes. Chosen simulation results, including maps and UAV trajectories constructed by the SLAM algorithm are included in the paper.

scholarly journals TERRESTRIAL MOBILE MAPPING BASED ON A MICROWAVE RADAR SENSOR. APPLICATION TO THE LOCALIZATION OF MOBILE ROBOTS

2021 ◽  
Vol V-1-2021 ◽  
pp. 73-80
Author(s):  
R. Rouveure ◽  
P. Faure ◽  
M.-O. Monod
Keyword(s):  
Global Positioning System ◽  
Mobile Robotics ◽  
Ease Of Use ◽  
Mobile Mapping ◽  
Microwave Radar ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Outdoor Environments ◽  
Slam Algorithm ◽  
Radar Map

Abstract. Mobile robotics applications in outdoor environments now use intensively Global Positioning System (GPS). For localization or navigation operations, GPS has become an essential tool due to its ease of use, its precision, and its worldwide accessibility. The increase of autonomy level in mobile robotics implies a robust centimeter-level positioning, but the presence of natural (trees, mountains) or man-made obstacles (buildings) can degrade or prevent GPS signals reception. We present in this paper a solution for robots localization based on PELICAN microwave radar. PELICAN radar provides each second a panoramic image of the surrounding environment. These images are concatenated through a Simultaneous Localization And Mapping (SLAM) algorithm in order to build global maps of the traveled environments. The proposed solution computes the position and orientation of the robot through a real-time 3D matching between the current radar image and a pre-existing radar map constructed during an exploratory phase.

scholarly journals An Efficient Rescue System with Online Multi-Agent SLAM Framework

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 235 ◽  
Author(s):  
SeungHwan Lee ◽  
HanJun Kim ◽  
BeomHee Lee
Keyword(s):  
Measurement Unit ◽  
The People ◽  
Localization And Mapping ◽  
Closest Point Method ◽  
Rescue System ◽  
Multi Agent ◽  
Slam Algorithm ◽  
The Individual ◽  
Rescue Time ◽  
Map Merging

A novel and an efficient rescue system with a multi-agent simultaneous localization and mapping (SLAM) framework is proposed to reduce the rescue time while rescuing the people trapped inside a burning building. In this study, the truncated signed distance (TSD) based SLAM algorithm is employed to accurately construct a two-dimensional map of the surroundings. For a new and significantly different scenario, information is gathered and the general iterative closest point method (GICP) is directly employed instead of the conventional TSD-SLAM process. Rescuers can utilize a total map created by merging individual maps, allowing them to efficiently search for victims. For online map merging, it is essential to determine the timing of when the individual maps are merged and the extent to which one map reflects the other map, via the weights. In the several experiments conducted, a light-detection and ranging system and an inertial measurement unit were integrated into a smart helmet for rescuers. The results indicated that the map was built more accurately than that obtained using the conventional TSD-SLAM. Additionally, the merged map was built more correctly by determining proper parameters for online map merging. Consequently, the accurate merged map allows rescuers to search for victims efficiently.

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