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 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 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.

A Pose-Graph Optimization tool for MATLAB

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

scholarly journals RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3955
Author(s):  
Jung-Cheng Yang ◽  
Chun-Jung Lin ◽  
Bing-Yuan You ◽  
Yin-Long Yan ◽  
Teng-Hu Cheng
Keyword(s):  
Feedback Control ◽  
Real Time ◽  
High Frequency ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Sampling Rate ◽  
Outdoor Environment ◽  
Position Accuracy ◽  
Graph Optimization ◽  
Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

scholarly journals Distributed mapping with privacy and communication constraints: Lightweight algorithms and object-based models

2017 ◽  
Vol 36 (12) ◽  
pp. 1286-1311 ◽  
Author(s):  
Siddharth Choudhary ◽  
Luca Carlone ◽  
Carlos Nieto ◽  
John Rogers ◽  
Henrik I Christensen ◽  
...  
Keyword(s):  
Distributed Algorithms ◽  
Field Experiments ◽  
Sensor Data ◽  
Test Facility ◽  
Two Stage ◽  
Noisy Information ◽  
Seidel Method ◽  
Object Based ◽  
Graph Optimization ◽  
Pose Graph Optimization

We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of a two-stage approach that already exists, where we use the Successive Over-Relaxation and the Jacobi Over-Relaxation as workhorses to split the computation among the robots. We also provide conditions under which the proposed distributed protocols converge to the solution of the centralized two-stage approach. As a second contribution, we extend the proposed distributed algorithms to work with the object-based map models. The use of object-based models avoids the exchange of raw sensor measurements (e.g. point clouds or RGB-D data) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss–Seidel method) has excellent performance. The Distributed Gauss–Seidel method requires minimal information exchange, has an anytime flavor, scales well to large teams (we demonstrate mapping with a team of 50 robots), is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems. The source code is available for download at https://cognitiverobotics.github.io/distributed-mapper/

scholarly journals A Trajectory-Based Approach to Multi-Session Underwater Visual SLAM Using Global Image Signatures

2019 ◽  
Vol 7 (8) ◽  
pp. 278 ◽  
Author(s):  
Antoni Burguera Burguera ◽  
Francisco Bonin-Font
Keyword(s):  
Feature Matching ◽  
Computational Cost ◽  
Visual Slam ◽  
Single Session ◽  
Search Region ◽  
Loop Detector ◽  
Localization And Mapping ◽  
Single Link ◽  
Random Sample Consensus ◽  
Visual Odometer

This paper presents a multi-session monocular Simultaneous Localization and Mapping (SLAM) approach focused on underwater environments. The system is composed of three main blocks: a visual odometer, a loop detector, and an optimizer. Single session loop closings are found by means of feature matching and Random Sample Consensus (RANSAC) within a search region. Multi-session loop closings are found by comparing hash-based global image signatures. The optimizer refines the trajectories and joins the different maps. Map joining preserves the trajectory structure by adding a single link between the joined sessions, making it possible to aggregate or disaggregate sessions whenever is necessary. All the optimization processes can be delayed until a certain number of loops has been found in order to reduce the computational cost. Experiments conducted in real subsea scenarios show the quality and robustness of this proposal.

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