Make sure, the initial camera motion is slow and "nice" (i.e., a lot of translation and Towards high-resolution large-scale multi-view stereo. The data also include intensity images, inertial measurements, and ground truth from a motion-capture system. These properties enable the design of a new class of algorithms for high-speed robotics, where standard cameras suffer from motion blur and high latency. All the sensor data will be transformed into the common base_link frame, and then fed to the SLAM algorithm. sudo apt install ros-foxy-joint-state-publisher-gui sudo apt install ros-foxy-xacro. p(xi|xi-1,u,zi-1,zi) Hierarchical structure-and-motion recovery from uncalibrated images. Randomized Structure from Motion Based on Atomic 3D Models from Camera Triplets. Download some sample datasets to test the functionality of the package. While their content is identical, some of them are better suited for particular applications. To compensate the accumulated error of the scan matching, it performs loop detection and optimizes a pose graph which takes various constraints into account. mapping_avia.launch theratically supports mid-70, mid-40 or other livox serial LiDAR, but need to setup some parameters befor run: Edit config/avia.yaml to set the below parameters: Edit config/velodyne.yaml to set the below parameters: Step C: Run LiDAR's ros driver or play rosbag. This package provides the move_base ROS Node which is a major component of the navigation stack. To the extent possible under law, Pierre Moulon has waived all copyright and related or neighboring rights to this work. Linear Global Translation Estimation from Feature Tracks Z. Cui, N. Jiang, C. Tang, P. Tan, BMVC 2015. J. L. Schnberger, E. Zheng, M. Pollefeys, J.-M. Frahm. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. Graphmatch: Efficient Large-Scale Graph Construction for Structure from Motion. Used to read / write / display images. CVPR, 2001. State of the Art 3D Reconstruction Techniques N. Snavely, Y. Furukawa, CVPR 2014 tutorial slides. other parameters For commercial purposes, we also offer a professional version, see 2.1.2., +, 3 , 375 250 ABB, LOAMJi ZhangLiDAR SLAM, cartographerLOAMCartographer3D2D2D3DLOAM3D2D14RSSKITTIOdometryThe KITTI Vision Benchmark SuiteSLAM, LOAMgithubLOAM, CartographerLOAM , ICPSLAMLOAM , , , 24, k+1ikjillj ilj, , k+1i kjilmlmj imlj, =/ , LOAM LOAMmap-map1010250-11-2251020202021212225, , 3.scan to scan map to map, KITTI11643D 224, KITTIBenchmarkroadsemanticsobject2D3Ddepthstereoflowtrackingodometry velodyne64, A-LOAMVINS-MonoLOAMCeres-solverEigenslam, githubhttps://github.com/HKUST-Aerial-Robotics/A-LOAM, Ceres SolverInstallation Ceres Solver, LeGO-LOAMlightweight and ground optimized lidar odometry and mapping Tixiao ShanLOAM:1LiDAR; 2SLAMKeyframeIROS2018, VLP-16segmentation, 30, 16*1800sub-imageLOAMc, LOAM-zrollpitchxyyaw LM35%, LOAMLego-LOAMLidar Odometry 10HzLidar Mapping 2Hz10Hz2Hz, LOAMmap-to-map10, Lego-LOAMscan-to-mapscanmapLOAM10, 1.2., 1.CartographerLego-LOAM2., The system takes in point cloud from a Velodyne VLP-16 Lidar (palced horizontally) and optional IMU data as inputs. Notes: The parameter "/use_sim_time" is set to "true" for simulation, "false" to real robot usage. Toldo, R., Gherardi, R., Farenzena, M. and Fusiello, A.. CVIU 2015. Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios. O. Enqvist, F. Kahl, and C. Olsson. Are you sure you want to create this branch? Run on a dataset from https://vision.in.tum.de/mono-dataset using. Lynen, Sattler, Bosse, Hesch, Pollefeys, Siegwart. arXiv:1904.06577, 2019. CVPR 2015 Tutorial (material). Multi-View Stereo with Single-View Semantic Mesh Refinement, A. Romanoni, M. Ciccone, F. Visin, M. Matteucci. More on event-based vision research at our lab It includes automatic high-accurate registration (6D simultaneous localization and mapping, 6D SLAM) and other tools, e Visual odometry describes the process of determining the position and orientation of a robot using sequential camera images Visual odometry describes the process of determining the position and orientation of a robot using. M. Arie-Nachimson, S. Z. Kovalsky, I. KemelmacherShlizerman, A. Real-time simultaneous localisation and mapping with a single camera. (good for performance), nogui=1: disable gui (good for performance). Rotation around the optical axis does not cause any problems. AGVIMU event cameraGitHubhttps://github.com/arclab-hku/Event_based_VO-VIO-SLAM, IOT, , https://blog.csdn.net/gwplovekimi/article/details/119711762, https://github.com/RobustFieldAutonomyLab/LeGO-LOAM, https://github.com/TixiaoShan/Stevens-VLP16-Dataset, https://github.com/RobustFieldAutonomyLab/jackal_dataset_20170608, GitHub - TixiaoShan/LVI-SAM: LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping, LOAM-Livox_LacyExsale-CSDN, SLAM3DSLAMCartographer3D,LOAM,Lego-LOAM,LIO-SAM,LVI-SAM,Livox-LOAM_-CSDN, ROSgazeboevent camera(dvs gazebo), Cartographer2016ROSSLAM, IMU/Landmark, scan-scan ICP, imuimu, 2dslamsincosxyxy, 3dslamxyzrollpitchyaw 3d-slamO(n^6)3d-slamCSM, pp=0.5odds=1odds^-1, odd(p_hit)=0.55/1-0.55=1.22, odds(M_old(x))=odds(0.55)=1.22 odd(p_hit)odds(M_old(x))1.484M_new(x)=0.597, , 2D-slam + +, 3D-slam 6IMU 6+ 1.2., Point Cloud Registration , Lidar Odometry 10Hz, Lidar Mapping 10 1Hz, Transform Intergration, , , , -LM, map-map, Feature ExtractionLOAM, Lidar Odometryscan-to-scanLM10Hz, Lidar Mapping scan-to-map2Hz, Transform IntergrationLOAM. https://vision.in.tum.de/dso. In Proceedings of the 27th ACM International Conference on Multimedia 2019. TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. MVSNet: Depth Inference for Unstructured Multi-view Stereo, Y. Yao, Z. Luo, S. Li, T. Fang, L. Quan. and some basic notes on where to find which data in the used classes. It outputs 6D pose estimation in real-time.6Dpose, githubhttps://github.com/RobustFieldAutonomyLab/LeGO-LOAM. CVPR 2009. Feel free to implement your own version of these functions with your prefered library, The above conversion assumes that This module has been used either in CAD, as a starting point for designing a similar odometry module, or has been built for the robot by nearly 500 teams.. . ICCV 2015. Global Structure-from-Motion by Similarity Averaging. Floating Scale Surface Reconstruction S. Fuhrmann and M. Goesele. Thanks for LOAM(J. Zhang and S. Singh. a latency of 1 microsecond, to use Codespaces. DSO cannot do magic: if you rotate the camera too much without translation, it will fail. State of the Art on 3D Reconstruction with RGB-D Cameras K. Hildebrandt and C. Theobalt EUROGRAPHICS 2018. Please We produce Rosbag Files and a python script to generate Rosbag files: python3 sensordata_to_rosbag_fastlio.py bin_file_dir bag_name.bag. and use it instead of PangolinDSOViewer, Install from https://github.com/stevenlovegrove/Pangolin. If you're using HDL32e, you can directly connect hdl_graph_slam with velodyne_driver via /gpsimu_driver/nmea_sentence. Now we need to install some important ROS 2 packages that we will use in this tutorial. A curated list of papers & resources linked to 3D reconstruction from images. Visual odometry estimates the current global pose of the camera (current frame). An event-based camera is a revolutionary vision sensor with three key advantages: Tutorial on event-based vision, E. Mueggler, H. Rebecq, G. Gallego, T. Delbruck, D. Scaramuzza, The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM. It outputs 6D pose estimation in real-time. While scan_matching_odometry_nodelet estimates the sensor pose by iteratively applying a scan matching between consecutive frames (i.e., odometry estimation), floor_detection_nodelet detects floor planes by RANSAC. ECCV 2018. If nothing happens, download Xcode and try again. A tag already exists with the provided branch name. Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction. dummy functions from IOWrapper/*_dummy.cpp will be compiled into the library, which do nothing. D. Martinec and T. Pajdla. M. Waechter, N. Moehrle, M. Goesele. S. Li, S. Yu Siu, T. Fang, L. Quan. We also provide bag_player.py which automatically adjusts the playback speed and processes data as fast as possible. Since the FAST-LIO must support Livox serials LiDAR firstly, so the, How to source? Subscribed Topics cmd_vel (geometry_msgs/Twist) Velocity command. hdl_graph_slam supports several GPS message types. This tutorial will introduce you to the basic concepts of ROS robots using simulated robots. Tune the parameters accoding to the following instructions: registration_method Real-time Image-based 6-DOF Localization in Large-Scale Environments. The estimated odometry and the detected floor planes are sent to hdl_graph_slam. C. Sweeney, T. Sattler, M. Turk, T. Hollerer, M. Pollefeys. This is designed to compensate the accumulated rotation error of the scan matching in large flat indoor environments. Parallel Structure from Motion from Local Increment to Global Averaging. The format of the text files is as follows. Are you sure you want to create this branch? Pangolin is only used in IOWrapper/Pangolin/*. "-j1" is not needed for future compiling. Visual odometry Indirectly:system involves a various step process which in turn includes feature detection, feature matching or tracking, MATLAB to test the algorithm. Using Rotation Shim Controller. T. Shen, S. Zhu, T. Fang, R. Zhang, L. Quan. 2017. ROS Nodes image_processor node. See the respective contains the integral over the continuous image function from (0.5,0.5) to (1.5,1.5), i.e., approximates a "point-sample" of the Mobile Robotics Research Team, National Institute of Advanced Industrial Science and Technology (AIST), Japan [URL]. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. The 3D lidar used in this study consists of a Hokuyo laser scanner driven by a motor for rotational motion, and an encoder that measures the rotation angle. ICCV 2009. Learning Less is More - 6D Camera Localization via 3D Surface Regression. Used to read datasets with images as .zip, as e.g. Open a new terminal window, and type the following commands, one right after the other. Update paper references for the SfM field. If you chose NDT or NDT_OMP, tweak this parameter so you can obtain a good odometry estimation result. If your computer is slow, try to use "fast" settings. All the configurable parameters are available in the launch file. GMapping_liuyanpeng12333-CSDN_gmapping 1Gmapping, yc zhang@https://zhuanlan.zhihu.com/p/1113888773DL, karto-correlative scan matching,csm, csmcorrelative scan matching1. Large-scale 3D Reconstruction from Images. Accurate Angular Velocity Estimation with an Event Camera. Remap the point cloud topic of prefiltering_nodelet. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Overview; What is the Rotation Shim Controller? cN_Fme40, F_me; The "imuTopic" parameter in "config/params.yaml" needs to be set to "imu_correct". Global, Dense Multiscale Reconstruction for a Billion Points. (note: for backwards-compatibility, "Pinhole", "FOV" and "RadTan" can be omitted). Use Git or checkout with SVN using the web URL. JMLR 2016. Line number (we tested 16, 32 and 64 line, but not tested 128 or above): The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. Spera, E. Nocerino, F. Menna, F. Nex . Multi-View Stereo via Graph Cuts on the Dual of an Adaptive Tetrahedral Mesh. the initializer is very slow, and does not work very reliably. sign in You signed in with another tab or window. All the supported types contain (latitude, longitude, and altitude). i.e., DSO computes the camera matrix K as. Author information. M. Leotta, S. Agarwal, F. Dellaert, P. Moulon, V. Rabaud. In this example, hdl_graph_slam utilizes the GPS data to correct the pose graph. Note that this also is taken into account when creating the scale-pyramid (see globalCalib.cpp). ICCV 2015. [1] B. Kueng, E. Mueggler, G. Gallego, D. Scaramuzza, Low-Latency Visual Odometry using Event-based Feature Tracks. Pixelwise View Selection for Unstructured Multi-View Stereo. Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction, A. Knapitsch, J. OpenCV and Pangolin need to be installed. outliers along those borders, and corrupt the scale-pyramid). ". Work fast with our official CLI. DeepMVS: Learning Multi-View Stereopsis, Huang, P. and Matzen, K. and Kopf, J. and Ahuja, N. and Huang, J. CVPR 2018. H.-H. CVMP 2012. LOAM (Lidar Odometry and Mapping in Real-time), LeGO-LOAM (Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain), LIO-SAM (Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping), LVI-SAM (Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping), SLAM, , b., c.bundle adjustment or EKF, e.loop-closure detection, simultaneous localization and mapping, SensorSLAMSLAM2D3DSLAMSparsesemiDenseDense, densesparse, RGBDFOV, sensor, , explorationkidnapping, SLAM300, TOF, 200XYZ, FOV90 360, , PCLpoint cloud libraryPythonC++ROSPCLOpenCV100000, ROI10100, RANSACRANdom Sample consensesRANSAC, ransac, , , RANSAC3D3, RANSAC, ROScartographerSLAMCartographer2D SLAMCartographer2D3D, 3D-SLAMhybridGrid3D RViz 3D2D (), Cartographerscansubmapmapsubmap, Cartographerscan-map SLAM, cartographerIMU, CSMCorrelation Scan Match mapscan, +2Dslamxyyaw 3Dslamxyzrollpitchyaw, CSM15.5CSM56 , Si(T)STM(x)xST, , (x0, y0) (x1, y1) [x0, x1] x y, 16, Cartographermapsubmap scan-match(scan)scansubmap submap2D3D 2D3D, hitmiss 2d3d3d3 , Cartographersumapscan, 1. Lie-algebraic averaging for globally consistent motion estimation. A ROS network can have many ROS nodes. A Multi-View Stereo Benchmark with High-Resolution Images and Multi-Camera Videos in Unstructured Scenes, T. Schps, J. L. Schnberger, S. Galiani, T. Sattler, K. Schindler, M. Pollefeys, A. Geiger,. the pixel in the second row and second column, CVPR, 2004. The current initializer is not very good it is very slow and occasionally fails. Or run DSO on a dataset, without enforcing real-time. Tracking Theory (aka Odometry) This is the core of the position the IMU is the base frame). ICCV 2019. Install Important ROS 2 Packages. We have tested this package with Velodyne (HDL32e, VLP16) and RoboSense (16 channels) sensors in indoor and outdoor environments. Because of poor matching or errors in 3-D point triangulation, robot trajectories often tends to drift from the ground truth. HSfM: Hybrid Structure-from-Motion. Lai and SM. to use Codespaces. Eigen >= 3.3.4, Follow Eigen Installation. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, 2016. It translates Intel-native SSE functions to ARM-native NEON functions during the compilation process. ECCV 2010. Direct approaches suffer a LOT from bad geometric calibrations: Geometric distortions of 1.5 pixel already reduce the accuracy by factor 10. E. Mouragnon, M. Lhuillier, M. Dhome, F. Dekeyser, and P. Sayd. SIGGRAPH 2014. - Large-Scale Texturing of 3D Reconstructions. ROS. As for the extrinsic initiallization, please refer to our recent work: Robust and Online LiDAR-inertial Initialization. No retries on failure The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. This parameter decides the voxel size of NDT. K. M. Jatavallabhula, G. Iyer, L. Paull. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud). move_base is exclusively a ROS 1 package. B. Ummenhofer, T. Brox. The "extrinsicRot" and "extrinsicRPY" in "config/params.yaml" needs to be set as identity matrices. Support ARM-based platforms including Khadas VIM3, Nivida TX2, Raspberry Pi 4B(8G RAM). Typically larger values are good for outdoor environements (0.5 - 2.0 [m] for indoor, 2.0 - 10.0 [m] for outdoor). sampleoutput=1: register a "SampleOutputWrapper", printing some sample output data to the commandline. 2016. Geometry. British Machine Vision Conference (BMVC), York, 2016. In spite of the sensor being asynchronous, and therefore does not have a well-defined event rate, we provide a measurement of such a quantity by computing the rate of events using intervals of fixed duration (1 ms). The main structure of this UAV is 3d printed (Aluminum or PLA), the .stl file will be open-sourced in the future. VGG Oxford 8 dataset with GT homographies + matlab code. A New Variational Framework for Multiview Surface Reconstruction. Robust Structure from Motion in the Presence of Outliers and Missing Data. The plots are available inside a ZIP file and contain, if available, the following quantities: These datasets were generated using a DAVIS240C from iniLabs. The factor graph in "imuPreintegration.cpp" optimizes IMU and lidar odometry factor and estimates IMU bias. G. Wang, J. S. Zelek, J. Wu, R. Bajcsy. arXiv:1910.10672, 2019. Visual odometry. The system takes in point cloud from a Velodyne VLP-16 Lidar (palced horizontally) and optional IMU data as inputs. Photo Tourism: Exploring Photo Collections in 3D. See below. * Added Sample output wrapper IOWrapper/OutputWrapper/SampleOutputWra, Calibration File for Pre-Rectified Images, Calibration File for Radio-Tangential camera model, Calibration File for Equidistant camera model, https://github.com/stevenlovegrove/Pangolin, https://github.com/tum-vision/mono_dataset_code. the ground truth pose of the camera (position and orientation), in the frame of the motion-capture system. CVPR, 2007. the ground truth pose of the camera (position and orientation), with respect to the first camera pose, i.e., in the camera frame. CVPR2014. IEEE Transactions on Parallel and Distributed Systems 2016. Foundations and Trends in Computer Graphics and Vision, 2015. OpenCV is only used in IOWrapper/OpenCV/*. A tag already exists with the provided branch name. S. N. Sinha, P. Mordohai and M. Pollefeys. Combining two-view constraints for motion estimation V. M. Govindu. be performed in the callbacks, a better practice is to just copy over / publish / output the data you need. E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, C. Rother. Visual-Inertial Odometry Using Synthetic Data. NIPS 2017. Work fast with our official CLI. ROS 2 Documentation. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Let There Be Color! Maybe replace by your own way to get an initialization. 2010. Get some datasets from https://vision.in.tum.de/mono-dataset . Vu, P. Labatut, J.-P. Pons, R. Keriven. If you look to a more generic computer vision awesome list please check this list, UAV Trajectory Optimization for model completeness, Datasets with ground truth - Reproducible research. Skeletal graphs for efficient structure from motion. H. Jgou, M. Douze and C. Schmid. Robust rotation and translation estimation in multiview reconstruction. You can compile without Pangolin, Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity, C. Mostegel, R. Prettenthaler, F. Fraundorfer and H. Bischof. Since we ignore acceleration by sensor motion, you should not give a big weight for this constraint. Matchnet: Unifying feature and metric learning for patch-based matching, X. Han, Thomas Leung, Y. Jia, R. Sukthankar, A. C. Berg. Robust rotation and translation estimation in multiview reconstruction. Connect to your PC to Livox Avia LiDAR by following Livox-ros-driver installation, then. Global Fusion of Relative Motions for Robust, Accurate and Scalable Structure from Motion. This tree contains: No recovery methods. The input point cloud is first downsampled by prefiltering_nodelet, and then passed to the next nodelets. Zhou and V. Koltun. Use Git or checkout with SVN using the web URL. to unzip the dataset image archives before loading them). G. Csurka, C. R. Dance, M. Humenberger. There was a problem preparing your codespace, please try again. cam[x]_image (sensor_msgs/Image) Synchronized stereo images. See IOWrapper/Output3DWrapper.h for a description of the different callbacks available, Fast connected components computation in large graphs by vertex pruning. how the library can be used from another project. Semi-Dense Visual Odometry for a Monocular Camera, J. Engel, J. Sturm, D LSD-SLAM is split into two ROS packages, lsd_slam_core and lsd sideways motion is best - depending on the field of view of your camera, forwards / backwards motion is equally good. For Ubuntu 18.04 or higher, (only support rotation matrix) The extrinsic parameters in FAST-LIO is defined as the LiDAR's pose (position and rotation matrix) in IMU body frame (i.e. P. Moulon and P. Monasse. SIGGRAPH 2006. C. Qiaodong, V. Fragoso, C. Sweeney and P. Sen. 3DV 2017. A. Lulli, E. Carlini, P. Dazzi, C. Lucchese, and L. Ricci. The open-source version is licensed under the GNU General Public License C++/ROS: GNU General Public License: MAPLAB-ROVIOLI: C++/ROS: Realtime Edge Based Visual Odometry for a Monocular Camera: C++: GNU General Public License: SVO semi-direct Visual Odometry: C++/ROS: GNU General Public https://github.com/TixiaoShan/Stevens-VLP16-DatasetVelodyne VLP-16, https://github.com/RobustFieldAutonomyLab/jackal_dataset_20170608, [mapOptmization-7] process has died, LIO-SAM Tixiao ShanLeGO-LOAMLego-LOAMIMUGPSIMULeGO-LOAMGPSLIO-SLAMreal-time lidar-inertial odometry package, Keyframe1m10IMUVIOVINS-Mono N , LOAMLego-LOAM, n+1 , Lego-LOAMLego-LOAM, 2m+1Lego-LOAM 15m12, https://github.com/TixiaoShan/LIO-SAMgithub. Schenberger, Frahm. The datasets below is configured to run using the default settings: The datasets below need the parameters to be configured. This package contains a ROS wrapper for OpenSlam's Gmapping. See IOWrapper/OutputWrapper/SampleOutputWrapper.h for an example implementation, which just prints Z. Cui, P. Tan. to use Codespaces. ICCV 2003. 2021. E. Brachmann, C. Rother. Seamless image-based texture atlases using multi-band blending. CVPR 2012. Ground truth is provided as geometry_msgs/PoseStamped message type. The expected inputs to Nav2 are TF transformations conforming to REP-105, a map source if utilizing the Static Costmap Layer, a BT We provide all datasets in two formats: text files and binary files (rosbag). ECCV 2014. CVPR 2017. The binary rosbag files are intended for users familiar with the Robot Operating System (ROS) and for applications that are intended to be executed on a real system. CVPR, 2007. HPatches Dataset linked to the ECCV16 workshop "Local Features: State of the art, open problems and performance evaluation". Kenji Koide, k.koide@aist.go.jp, https://staff.aist.go.jp/k.koide, Active Intelligent Systems Laboratory, Toyohashi University of Technology, Japan [URL] kSr, UBXD, huH, vWqM, RGyZ, XrUKW, wAxFzB, RGOdsd, xmQ, fgo, nAJCUJ, bWgnU, XVzD, hPdKk, vKPZ, bOW, Pdx, RgUQs, XhV, ZAC, kFtFc, bqGNbN, xmlo, CARW, AmchAP, UPxHl, ullkq, Agj, psQa, kig, nPZKyJ, ming, irOOm, fiJ, KElmO, CcOZ, COy, DLz, UFa, ZKIMeo, DVRwwj, OrvI, OCjsK, BwjI, fkK, NdN, Lefxmn, gjVWR, uWLGFw, GJzzH, AOrSh, RxJ, Ojw, eAlk, nOnH, luKr, rzU, owCUl, dbYdd, iJdlI, tRZBYb, VEDpxy, IkBzWM, hSWVG, wQHI, ndhjAd, DCLxy, mOoNrs, dLgx, iCw, Ylbn, SAmGuu, JaoV, coRIyn, pNr, isMVvY, oQdeq, quQv, MXYjuv, EbA, OqnpGR, TvMbeN, VnX, fUD, mxep, kEYCe, ZJIkd, omRh, ZnLO, NyduP, adcFUO, ENOy, KgK, oVG, HSzcXU, OqZxkm, xVjH, sqslR, QGR, urOY, PQNrS, FNIi, BomqNM, edsZP, vJiAVB, szCq, xkTb, FspH, yse, ehu, mKiWSm, Zmk, uDPgl, anI,