scholarly journals 2-D UAV navigation solution with LIDAR sensor under GPS-denied environment

2021 ◽  
Vol 2120 (1) ◽  
pp. 012026
Author(s):  
J C Ho ◽  
S K Phang ◽  
H K Mun
Keyword(s):  
Processing Speed ◽  
Research Work ◽  
Ground Truth ◽  
Lidar Data ◽  
Loop Closure ◽  
Localization And Mapping ◽  
Graph Optimization ◽  
Aerial Vehicle ◽  
Pose Graph Optimization ◽  
Camera Sensors

Abstract Unmanned aerial vehicle (UAV) is widely used by many industries these days such as militaries, agriculture, and surveillance. However, one of the main challenges of UAV is navigating through an environment where global positioning system (GPS) is being denied. The main purpose of this paper is to find a solution for UAV to be able to navigate in a GPS denied surrounding without affecting the drone flight performance. There are two ways to overcome these challenges such as using visual odometry (VO) or by using simultaneous localization and mapping (SLAM). However, VO has a drawback because camera sensors require good lighting which will affect the performance of the UAV when it is navigating through a low light intensity environment. Hence, in this paper 2-D SLAM will be use as a solution to help UAV to navigate under a GPS-denied environment with the help of a light detection and ranging (LIDAR) sensor which known as a LIDAR-based SLAM. This is because SLAM can help UAVs to localize itself and map the surrounding of the environment. The concept and idea of this paper will be fully simulated using MATLAB, where the drone navigation will be simulated in MATLAB to extract LIDAR data and to use the LIDAR data to carry out SLAM via pose graph optimization. Besides, the contribution to this research work has also identified that in pose graph optimization, the loop closure threshold and loop closure radius play an important role. The loop closure threshold can affect the accuracy of the trajectory of the drone and the accuracy of mapping the environment as compared to ground truth. On the other hand, the loop closure search radius can increase the processing speed of obtaining the data via pose graph optimization. The main contribution to this research work is shown that the processing speed can increase up to 45 % and the accuracy of the trajectory of the drone and the mapped surrounding is quite accurate as compared to ground truth.

scholarly journals A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization

2021 ◽  
Vol 13 (14) ◽  
pp. 2720
Author(s):  
Shoubin Chen ◽  
Baoding Zhou ◽  
Changhui Jiang ◽  
Weixing Xue ◽  
Qingquan Li
Keyword(s):  
Point Clouds ◽  
Visual Features ◽  
Visual Slam ◽  
Key Factors ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Active Sensor ◽  
Localization And Mapping ◽  
Graph Optimization ◽  
Pose Graph Optimization

LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.

scholarly journals Research on Visual Positioning of a Roadheader and Construction of an Environment Map

2021 ◽  
Vol 11 (11) ◽  
pp. 4968
Author(s):  
Wentao Zhang ◽  
Guodong Zhai ◽  
Zhongwen Yue ◽  
Tao Pan ◽  
Ran Cheng
Keyword(s):  
Open Source ◽  
Optimal Estimation ◽  
Calculation Model ◽  
The Body ◽  
Elastic Band ◽  
Visual Positioning ◽  
Localization And Mapping ◽  
Graph Optimization ◽  
Random Sample Consensus ◽  
Pose Graph Optimization

The autonomous positioning of tunneling equipment is the key to intellectualization and robotization of a tunneling face. In this paper, a method based on simultaneous localization and mapping (SLAM) to estimate the body pose of a roadheader and build a navigation map of a roadway is presented. In terms of pose estimation, an RGB-D camera is used to collect images, and a pose calculation model of a roadheader is established based on random sample consensus (RANSAC) and iterative closest point (ICP); constructing a pose graph optimization model with closed-loop constraints. An iterative equation based on Levenberg–Marquadt is derived-, which can achieve the optimal estimation of the body pose. In terms of mapping, LiDAR is used to experimentally construct the grid map based on open-source algorithms, such as Gmapping, Cartographer, Karto, and Hector. A point cloud map, octree map, and compound map are experimentally constructed based on the open-source library RTAB-MAP. By setting parameters, such as the expansion radius of an obstacle and the updating frequency of the map, a cost map for the navigation of a roadheader is established. Combined with algorithms, such as Dijskra and timed-elastic-band, simulation experiments show that the combination of octree map and cost map can support global path planning and local obstacle avoidance.

scholarly journals Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092530
Author(s):  
Feng Youyang ◽  
Wang Qing ◽  
Yang Gaochao
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Bayes Factor ◽  
Simulated Data ◽  
Schur Complement ◽  
Factor Graph ◽  
Data Sets ◽  
Loop Closure ◽  
Graph Optimization ◽  
Pose Graph Optimization

Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

scholarly journals Multi-Objective Optimization of Loop Closure Detection Parameters for Indoor 2D Simultaneous Localization and Mapping

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1906
Author(s):  
Dongxiao Han ◽  
Yuwen Li ◽  
Tao Song ◽  
Zhenyang Liu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Optimization Method ◽  
Evaluation Metrics ◽  
Multi Objective Optimization ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multi Objective ◽  
Evaluation Approach ◽  
Localization And Mapping

Aiming at addressing the issues related to the tuning of loop closure detection parameters for indoor 2D graph-based simultaneous localization and mapping (SLAM), this article proposes a multi-objective optimization method for these parameters. The proposed method unifies the Karto SLAM algorithm, an efficient evaluation approach for map quality with three quantitative metrics, and a multi-objective optimization algorithm. More particularly, the evaluation metrics, i.e., the proportion of occupied grids, the number of corners and the amount of enclosed areas, can reflect the errors such as overlaps, blurring and misalignment when mapping nested loops, even in the absence of ground truth. The proposed method has been implemented and validated by testing on four datasets and two real-world environments. For all these tests, the map quality can be improved using the proposed method. Only loop closure detection parameters have been considered in this article, but the proposed evaluation metrics and optimization method have potential applications in the automatic tuning of other SLAM parameters to improve the map quality.

scholarly journals OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

2021 ◽  
Author(s):  
Xieyuanli Chen ◽  
Thomas Läbe ◽  
Andres Milioto ◽  
Timo Röhling ◽  
Jens Behley ◽  
...  
Keyword(s):  
Autonomous Systems ◽  
Urban Environments ◽  
Lidar Data ◽  
Global Localization ◽  
Loop Closure ◽  
Siamese Network ◽  
Localization And Mapping ◽  
Different Seasons ◽  
Yaw Angle ◽  
Loop Closing

AbstractLocalization and mapping are key capabilities of autonomous systems. In this paper, we propose a modified Siamese network to estimate the similarity between pairs of LiDAR scans recorded by autonomous cars. This can be used to address both, loop closing for SLAM and global localization. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data. It estimates the similarity between pairs of scans using the concept of image overlap generalized to range images and furthermore provides a relative yaw angle estimate. Based on such predictions, our method is able to detect loop closures in a SLAM system or to globally localize in a given map. For loop closure detection, we use the overlap prediction as the similarity measurement to find loop closure candidates and integrate the candidate selection into an existing SLAM system to improve the mapping performance. For global localization, we propose a novel observation model using the predictions provided by OverlapNet and integrate it into a Monte-Carlo localization framework. We evaluate our approach on multiple datasets collected using different LiDAR scanners in various environments. The experimental results show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods and that it generalizes well to different environments. Furthermore, our method reliably localizes a vehicle in typical urban environments globally using LiDAR data collected in different seasons.

scholarly journals Real-Time Vehicle Positioning and Mapping Using Graph Optimization

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2815
Author(s):  
Anweshan Das ◽  
Jos Elfring ◽  
Gijs Dubbelman
Keyword(s):  
Real Time ◽  
Rural Areas ◽  
Low Cost ◽  
Ground Truth ◽  
Visual Odometry ◽  
Graph Optimization ◽  
Urban And Rural Areas ◽  
Vehicle Positioning ◽  
Rate Sensor ◽  
Pose Graph Optimization

In this work, we propose and evaluate a pose-graph optimization-based real-time multi-sensor fusion framework for vehicle positioning using low-cost automotive-grade sensors. Pose-graphs can model multiple absolute and relative vehicle positioning sensor measurements and can be optimized using nonlinear techniques. We model pose-graphs using measurements from a precise stereo camera-based visual odometry system, a robust odometry system using the in-vehicle velocity and yaw-rate sensor, and an automotive-grade GNSS receiver. Our evaluation is based on a dataset with 180 km of vehicle trajectories recorded in highway, urban, and rural areas, accompanied by postprocessed Real-Time Kinematic GNSS as ground truth. We compare the architecture’s performance with (i) vehicle odometry and GNSS fusion and (ii) stereo visual odometry, vehicle odometry, and GNSS fusion; for offline and real-time optimization strategies. The results exhibit a 20.86% reduction in the localization error’s standard deviation and a significant reduction in outliers when compared with automotive-grade GNSS receivers.

scholarly journals Evaluation of Salinity Intrusion in Arable Lands of Al-Batinah Coastal Belt Using Unmanned Aerial Vehicle (UAV) Color Imagery

2020 ◽  
Vol 24 ◽  
pp. 44
Author(s):  
Sawsana Hilal Al-Rahbi ◽  
Yaseen Ahmed Al-Mulla ◽  
Hemanatha Jayasuriya
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Research Work ◽  
Ground Truth ◽  
Accurate Method ◽  
Aerial Images ◽  
Salinity Intrusion ◽  
Strong Negative Correlation ◽  
Coastal Belt ◽  
Salinity Levels ◽  
Aerial Vehicle

Salinity by seawater intrusion due to excess groundwater pumping for irrigation is a major environmental challenge in the coastal areas of Oman. Increasing salinity levels moving inward the arable lands is happening in a rapid manner. Thus, salinity needs to be evaluated and quantified using a fast and accurate method. The objective of this study is to estimate salinity intrusion in Al-Batinah coastal belt using color aerial imaging. The study was conducted in five randomly selected sites with increasingly different distances from the seashore of Al-Suwaiq area in Al-Batinah region. Color aerial images were acquired for each site by an Unmanned Aerial Vehicle (UAV). Images were enhanced by orthorectification in ENVI software. Green Leaf Index (GLI) was obtained from each site image using Matlab software. Image analysis results were compared with the results of analyzed soil and water samples taken for ground-truth verification. There was a strong negative correlation between the distance from the seashore and the soil EC of each site (R = -0.95). Similarly, the mean value of GLI increased as the salinity levels decreased, R= -0.96 and -0.92 for soil EC and water EC, respectively. The findings of this research work demonstrated the possibility of accurately use of color images taken by a UAV to quantify the salinity effect on vegetation along the costal belt.

The COBRA fixed-wing georeferenced imagery dataset

2015 ◽  
Vol 3 (2/3) ◽  
pp. 62-71 ◽  
Author(s):  
Sajad Saeedi ◽  
Carl Thibault ◽  
Michael Trentini ◽  
Howard Li
Keyword(s):  
Ground Truth ◽  
Target Localization ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
Robot Operating System ◽  
Benchmark Data ◽  
Localization And Mapping ◽  
Aerial Vehicle ◽  
Sensor Configuration

Purpose – The purpose of this paper is to present a localization and mapping data set acquired by a fixed-wing unmanned aerial vehicle (UAV). The data set was collected for educational and research purposes: to save time in dealing with hardware and to compare the results with a benchmark data set. The data were collected in standard Robot Operating System (ROS) format. The environment, fixed-wing, and sensor configuration are explained in detail. GPS coordinates of the fixed-wing are also available as ground truth. The data set is available for download (www.ece.unb.ca/COBRA/open_source.htm). Design/methodology/approach – The data were collected in standard ROS format. The environment, fixed-wing, and sensor configuration are explained in detail. Findings – The data set can be used for target localization and mapping. The data were collected to assist algorithm developments and help researchers to compare their results. Robotic data sets are specifically important when they are related to unmanned systems such as fixed-wing aircraft. Originality/value – The Robotics Data Set Repository (RADISH) by A. Howard and N. Roy hosts 41 well-known data sets with different sensors; however, there is no fixed-wing data set in RADISH. This work presents two data sets collected by a fixed-wing aircraft using ROS standards. The data sets can be used for target localization and SLAM.

A Pose-Graph Optimization tool for MATLAB

2018 ◽  
Author(s):  
João Carlos Virgolino Soares ◽  
Marco Antonio Meggiolaro
Keyword(s):  
Graph Optimization ◽  
Pose Graph Optimization
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