Our extensive experiments on three standard datasets, Replica, ScanNet, and TUM RGB-D show that ESLAM improves the accuracy of 3D reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to 10 times faster and does not require any pre-training. What is your RBG login name? You will usually have received this informiation via e-mail, or from the Infopoint or Help desk staff. While previous datasets were used for object recognition, this dataset is used to understand the geometry of a scene. 001). This repository is for Team 7 project of NAME 568/EECS 568/ROB 530: Mobile Robotics of University of Michigan. tum. Share study experience about Computer Vision, SLAM, Deep Learning, Machine Learning, and RoboticsRGB-live . Мюнхенський технічний університет (нім. 4. The ICL-NUIM dataset aims at benchmarking RGB-D, Visual Odometry and SLAM algorithms. in. We may remake the data to conform to the style of the TUM dataset later. 0. The TUM RGB-D dataset consists of RGB and depth images (640x480) collected by a Kinect RGB-D camera at 30 Hz frame rate and camera ground truth trajectories obtained from a high precision motion capture system. 15th European Conference on Computer Vision, September 8 – 14, 2018 | Eccv2018 - Eccv2018. We evaluated ReFusion on the TUM RGB-D dataset [17], as well as on our own dataset, showing the versatility and robustness of our approach, reaching in several scenes equal or better performance than other dense SLAM approaches. the initializer is very slow, and does not work very reliably. tum. : to open or tease out (wool) before carding. tum. 159. Visual Odometry is an important area of information fusion in which the central aim is to estimate the pose of a robot using data collected by visual sensors. Two different scenes (the living room and the office room scene) are provided with ground truth. SUNCG is a large-scale dataset of synthetic 3D scenes with dense volumetric annotations. 5. Registered on 7 Dec 1988 (34 years old) Registered to de. , at MI HS 1, Friedrich L. In this paper, we present the TUM RGB-D bench-mark for visual odometry and SLAM evaluation and report on the first use-cases and users of it outside our own group. TUMs lecture streaming service, in beta since summer semester 2021. This paper uses TUM RGB-D dataset containing dynamic targets to verify the effectiveness of the proposed algorithm. Choi et al. RBG. Rum Tum Tugger is a principal character in Cats. 822841 fy = 542. , illuminance and varied scene settings, which include both static and moving object. Volumetric methods with ours also show good generalization on the 7-Scenes and TUM RGB-D datasets. The benchmark contains a large. in. io. X and OpenCV 3. The button save_traj saves the trajectory in one of two formats (euroc_fmt or tum_rgbd_fmt). Among various SLAM datasets, we've selected the datasets provide pose and map information. However, most visual SLAM systems rely on the static scene assumption and consequently have severely reduced accuracy and robustness in dynamic scenes. This is forked from here, thanks for author's work. 3 ms per frame in dynamic scenarios using only an Intel Core i7 CPU, and achieves comparable. 89 papers with code • 0 benchmarks • 20 datasets. Experiments on public TUM RGB-D dataset and in real-world environment are conducted. We recommend that you use the 'xyz' series for your first experiments. in. rbg. 0/16 (Route of ASN) PTR: unicorn. de. Maybe replace by your own way to get an initialization. 159. 0/16 (Route of ASN) Recent Screenshots. DE zone. © RBG Rechnerbetriebsgruppe Informatik, Technische Universität München, 2013–2018, rbg@in. Wednesday, 10/19/2022, 05:15 AM. The TUM RGB-D dataset , which includes 39 sequences of offices, was selected as the indoor dataset to test the SVG-Loop algorithm. For each incoming frame, we. . WHOIS for 131. de. It is able to detect loops and relocalize the camera in real time. The RGB-D dataset[3] has been popular in SLAM research and was a benchmark for comparison too. idea","path":". TUM rgb-d data set contains rgb-d image. Two different scenes (the living room and the office room scene) are provided with ground truth. We select images in dynamic scenes for testing. mine which regions are static and dynamic relies only on anIt can effectively improve robustness and accuracy in dynamic indoor environments. tum. : to card (wool) as a preliminary to finer carding. Downloads livestrams from live. /build/run_tum_rgbd_slam Allowed options: -h, --help produce help message -v, --vocab arg vocabulary file path -d, --data-dir arg directory path which contains dataset -c, --config arg config file path --frame-skip arg (=1) interval of frame skip --no-sleep not wait for next frame in real time --auto-term automatically terminate the viewer --debug debug mode -. , in LDAP and X. RBG – Rechnerbetriebsgruppe Mathematik und Informatik Helpdesk: Montag bis Freitag 08:00 - 18:00 Uhr Telefon: 18018 Mail: [email protected]. 2022 from 14:00 c. No incoming hits Nothing talked to this IP. AS209335 - TUM-RBG, DE Note: An IP might be announced by multiple ASs. The experiments are performed on the popular TUM RGB-D dataset . Our method named DP-SLAM is implemented on the public TUM RGB-D dataset. The results indicate that the proposed DT-SLAM (mean RMSE = 0:0807. stereo, event-based, omnidirectional, and Red Green Blue-Depth (RGB-D) cameras. TUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of MunichIn the experiment, the mainstream public dataset TUM RGB-D was used to evaluate the performance of the SLAM algorithm proposed in this paper. We increased the localization accuracy and mapping effects compared with two state-of-the-art object SLAM algorithms. , illuminance and varied scene settings, which include both static and moving object. and TUM RGB-D [42], our framework is shown to outperform both monocular SLAM system (i. The freiburg3 series are commonly used to evaluate the performance. Login (with in. 289. To our knowledge, it is the first work combining the deblurring network into a Visual SLAM system. It defines the top of an enterprise tree for local Object-IDs (e. manhardt, nassir. ORB-SLAM2是一套完整的SLAM方案，提供了单目，双目和RGB-D三种接口。. GitHub Gist: instantly share code, notes, and snippets. © RBG Rechnerbetriebsgruppe Informatik, Technische Universität München, 2013–2018, rbg@in. amazing list of colors!. The datasets we picked for evaluation are listed below and the results are summarized in Table 1. If you want to contribute, please create a pull request and just wait for it to be. The measurement of the depth images is millimeter. rbg. Example result (left are without dynamic object detection or masks, right are with YOLOv3 and masks), run on rgbd_dataset_freiburg3_walking_xyz: Getting Started. RBG – Rechnerbetriebsgruppe Mathematik und Informatik Helpdesk: Montag bis Freitag 08:00 - 18:00 Uhr Telefon: 18018 Mail: rbg@in. The TUM RGB-D dataset , which includes 39 sequences of offices, was selected as the indoor dataset to test the SVG-Loop algorithm. This project was created to redesign the Livestream and VoD website of the RBG-Multimedia group. )We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30. , chairs, books, and laptops) can be used by their VSLAM system to build a semantic map of the surrounding. The second part is in the TUM RGB-D dataset, which is a benchmark dataset for dynamic SLAM. bash scripts/download_tum. A bunch of physics-based weirdos fight it out on an island, everything is silly and possibly a bit. The standard training and test set contain 795 and 654 images, respectively. We select images in dynamic scenes for testing. The persons move in the environments. t. Mystic Light. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. Map: estimated camera position (green box), camera key frames (blue boxes), point features (green points) and line features (red-blue endpoints){"payload":{"allShortcutsEnabled":false,"fileTree":{"":{"items":[{"name":". de and the Knowledge Database kb. The KITTI dataset contains stereo sequences recorded from a car in urban environments, and the TUM RGB-D dataset contains indoor sequences from RGB-D cameras. TUMs lecture streaming service, currently serving up to 100 courses every semester with up to 2000 active students. In this part, the TUM RGB-D SLAM datasets were used to evaluate the proposed RGB-D SLAM method. 0. Freiburg3 consists of a high-dynamic scene sequence marked 'walking', in which two people walk around a table, and a low-dynamic scene sequence marked 'sitting', in which two people sit in chairs with slight head or part of the limb. The TUM dataset is a well-known dataset for evaluating SLAM systems in indoor environments. the workspaces in the offices. Compared with ORB-SLAM2, the proposed SOF-SLAM achieves averagely 96. Many answers for common questions can be found quickly in those articles. TUM RBG-D dynamic dataset. 0/16 Abuse Contact data. In order to ensure the accuracy and reliability of the experiment, we used two different segmentation methods. The computer running the experiments features an Ubuntu 14. After training, the neural network can realize 3D object reconstruction from a single [8] , [9] , stereo [10] , [11] , or collection of images [12] , [13] . Monocular SLAM PTAM [18] is a monocular, keyframe-based SLAM system which was the first work to introduce the idea of splitting camera tracking and mapping into parallel threads, and. 3% and 90. This repository is linked to the google site. Livestream on Artemis → Lectures or live. Information Technology Technical University of Munich Arcisstr. de show that tumexam. For interference caused by indoor moving objects, we add the improved lightweight object detection network YOLOv4-tiny to detect dynamic regions, and the dynamic features in the dynamic area are then eliminated in the algorithm. 6 displays the synthetic images from the public TUM RGB-D dataset. The format of the RGB-D sequences is the same as the TUM RGB-D Dataset and it is described here. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. Please submit cover letter and resume together as one document with your name in document name. /build/run_tum_rgbd_slam Allowed options: -h, --help produce help message -v, --vocab arg vocabulary file path -d, --data-dir arg directory path which contains dataset -c, --config arg config file path --frame-skip arg (=1) interval of frame skip --no-sleep not wait for next frame in real time --auto-term automatically terminate the viewer --debug. This repository is linked to the google site. © RBG Rechnerbetriebsgruppe Informatik, Technische Universität München, 2013–2018, rbg@in. tum. The TUM RGB-D benchmark [5] consists of 39 sequences that we recorded in two different indoor environments. See the list of other web pages hosted by TUM-RBG, DE. de and the Knowledge Database kb. 2 On ucentral-Website; 1. However, they lack visual information for scene detail. de / [email protected](PTR record of primary IP) Recent Screenshots. , fr1/360). RGB-D dataset and benchmark for visual SLAM evaluation: Rolling-Shutter Dataset: SLAM for Omnidirectional Cameras: TUM Large-Scale Indoor (TUM LSI) Dataset:ORB-SLAM2的编译运行以及TUM数据集测试. Our method named DP-SLAM is implemented on the public TUM RGB-D dataset. An Open3D Image can be directly converted to/from a numpy array. 德国慕尼黑工业大学TUM计算机视觉组2012年提出了一个RGB-D数据集，是目前应用最为广泛的RGB-D数据集。数据集使用Kinect采集，包含了depth图像和rgb图像，以及ground truth等数据，具体格式请查看官网。on the TUM RGB-D dataset. Visual SLAM Visual SLAM In Simultaneous Localization And Mapping, we track the pose of the sensor while creating a map of the environment. 01:00:00. Two consecutive key frames usually involve sufficient visual change. Two key frames are. This paper presents this extended version of RTAB-Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real-world datasets (e. We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. net. It not only can be used to scan high-quality 3D models, but also can satisfy the demand. 02:19:59. We provided an. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article. de) or your attending physician can advise you in this regard. 756098Evaluation on the TUM RGB-D dataset. It is perfect for portrait shooting, wedding photography, product shooting, YouTube, video recording and more. However, only a small number of objects (e. This is an urban sequence with multiple loop closures that ORB-SLAM2 was able to successfully detect. libs contains options for training, testing and custom dataloaders for TUM, NYU, KITTI datasets. Tracking: Once a map is initialized, the pose of the camera is estimated for each new RGB-D image by matching features in. However, the pose estimation accuracy of ORB-SLAM2 degrades when a significant part of the scene is occupied by moving ob-jects (e. VPN-Connection to the TUM set up of the RBG certificate Furthermore the helpdesk maintains two websites. : You need VPN ( VPN Chair) to open the Qpilot Website. 19 IPv6: 2a09:80c0:92::19: Live Screenshot Hover to expand. Registrar: RIPENCC Route: 131. Moreover, our approach shows a 40. tum. g. This repository is a fork from ORB-SLAM3. Year: 2009; Publication: The New College Vision and Laser Data Set; Available sensors: GPS, odometry, stereo cameras, omnidirectional camera, lidar; Ground truth: No The TUM RGB-D dataset [39] con-tains sequences of indoor videos under different environ-ment conditions e. idea. The RGB-D case shows the keyframe poses estimated in sequence fr1 room from the TUM RGB-D Dataset [3], andNote. We provide examples to run the SLAM system in the KITTI dataset as stereo or monocular, in the TUM dataset as RGB-D or monocular, and in the EuRoC dataset as stereo or monocular. employs RGB-D sensor outputs and performs 3D camera pose estimation and tracking to shape a pose graph. PS: This is a work in progress, due to limited compute resource, I am yet to finetune the DETR model and standard vision transformer on TUM RGB-D dataset and run inference. The proposed V-SLAM has been tested on public TUM RGB-D dataset. txt; DETR Architecture . RGB Fusion 2. We select images in dynamic scenes for testing. 53% blue. Experiments were performed using the public TUM RGB-D dataset [30] and extensive quantitative evaluation results were given. Maybe replace by your own way to get an initialization. The ground-truth trajectory is obtained from a high-accuracy motion-capture system. Our experimental results have showed the proposed SLAM system outperforms the ORB. The TUM RGB-D dataset [39] con-tains sequences of indoor videos under different environ-ment conditions e. Diese sind untereinander und mit zwei weiteren Stratum 2 Zeitservern (auch bei der RBG gehostet) in einem Peerverband. tum. foswiki. Once this works, you might want to try the 'desk' dataset, which covers four tables and contains several loop closures. The TUM RGB-D dataset, published by TUM Computer Vision Group in 2012, consists of 39 sequences recorded at 30 frames per second using a Microsoft Kinect sensor in different indoor scenes. pcd格式保存，以便下一步的处理。环境：Ubuntu16. 5 Notes. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory. The video sequences are recorded by an RGB-D camera from Microsoft Kinect at a frame rate of 30 Hz, with a resolution of 640 × 480 pixel. The dynamic objects have been segmented and removed in these synthetic images. g. The video shows an evaluation of PL-SLAM and the new initialization strategy on a TUM RGB-D benchmark sequence. The depth here refers to distance. We use the calibration model of OpenCV. Abstract-We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs. 4-linux - optimised for Linux; 2. In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. The RGB-D dataset contains the following. The sequence selected is the same as the one used to generate Figure 1 of the paper. © RBG Rechnerbetriebsgruppe Informatik, Technische Universität München, 2013–2018, [email protected] guide The RBG Helpdesk can support you in setting up your VPN. Mystic Light. You can run Co-SLAM using the code below: TUM RGB-D SLAM Dataset and Benchmarkの導入をしました。 Open3DのRGB-D Odometryを用いてカメラの軌跡を求めるプログラムを作成しました。 評価ツールを用いて、ATEの結果をまとめました。 これでSLAMの評価ができるようになりました。 We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. from publication: Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark. DE top-level domain. : You need VPN ( VPN Chair) to open the Qpilot Website. Our abuse contact API returns data containing information. tum. Among various SLAM datasets, we've selected the datasets provide pose and map information. Run. sh . tum. de TUM-RBG, DE. de Im Beschaffungswesen stellt die RBG die vergaberechtskonforme Beschaffung von Hardware und Software sicher und etabliert und betreut TUM-weite Rahmenverträge und zugehörige Webshops. color. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. 4. TUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of Munich Here you will find more information and instructions for installing the certificate for many operating systems: SSH-Server lxhalle. Additionally, the object running on multiple threads means the current frame the object is processing can be different than the recently added frame. This is an urban sequence with multiple loop closures that ORB-SLAM2 was able to successfully detect. 22 Dec 2016: Added AR demo (see section 7). The depth here refers to distance. The sequences include RGB images, depth images, and ground truth trajectories. in. 2-pack RGB lights can fill light in multi-direction. Color images and depth maps. Authors: Raza Yunus, Yanyan Li and Federico Tombari ManhattanSLAM is a real-time SLAM library for RGB-D cameras that computes the camera pose trajectory, a sparse 3D reconstruction (containing point, line and plane features) and a dense surfel-based 3D reconstruction. © RBG Rechnerbetriebsgruppe Informatik, Technische Universität München, 2013–2018, [email protected]. It contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. 1 TUM RGB-D Dataset. An Open3D Image can be directly converted to/from a numpy array. Laser and Lidar generate a 2D or 3D point cloud specifically. X. Material RGB and HEX color codes of TUM colors. Demo Running ORB-SLAM2 on TUM RGB-D DatasetOrb-Slam 2 Repo by the Author: RGB-D for Self-Improving Monocular SLAM and Depth Prediction Lokender Tiwari1, Pan Ji 2, Quoc-Huy Tran , Bingbing Zhuang , Saket Anand1,. 21 80333 Munich Germany +49 289 22638 +49. TUM RGB-D Scribble-based Segmentation Benchmark Description. Next, run NICE-SLAM. The second part is in the TUM RGB-D dataset, which is a benchmark dataset for dynamic SLAM. Thus, there will be a live stream and the recording will be provided. 80% / TKL Keyboards (Tenkeyless) As the name suggests, tenkeyless mechanical keyboards are essentially standard full-sized keyboards without a tenkey / numberpad. in. We extensively evaluate the system on the widely used TUM RGB-D dataset, which contains sequences of small to large-scale indoor environments, with respect to different parameter combinations. 0 is a lightweight and easy-to-set-up Windows tool that works great for Gigabyte and non-Gigabyte users who’re just starting out with RGB synchronization. Visual Simultaneous Localization and Mapping (SLAM) is very important in various applications such as AR, Robotics, etc. 1. idea. There are two persons sitting at a desk. Living room has 3D surface ground truth together with the depth-maps as well as camera poses and as a result perfectly suits not just for benchmarking camera trajectory but also reconstruction. tum. Our methodTUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of Munichon RGB-D data. Compared with art-of-the-state methods, experiments on the TUM RBG-D dataset, KITTI odometry dataset, and practical environment show that SVG-Loop has advantages in complex environments with. 159. This is not shown. de. Awesome SLAM Datasets. AS209335 TUM-RBG, DE. The key constituent of simultaneous localization and mapping (SLAM) is the joint optimization of sensor trajectory estimation and 3D map construction. M. TUM school of Engineering and Design Photogrammetry and Remote Sensing Arcisstr. 24 Live Screenshot Hover to expand. This dataset is a standard RGB-D dataset provided by the Computer Vision Class group of Technical University of Munich, Germany, and it has been used by many scholars in the SLAM. Covisibility Graph: A graph consisting of key frame as nodes. We integrate our motion removal approach with the ORB-SLAM2 [email protected] file rgb. tum. tum. de TUM-RBG, DE. The TUM RGB-D dataset provides many sequences in dynamic indoor scenes with accurate ground-truth data. SLAM and Localization Modes. TKL keyboards are great for small work areas or users who don't rely on a tenkey. Living room has 3D surface ground truth together with the depth-maps as well as camera poses and as a result perfectly suits not just for benchmarking camera. RGB and HEX color codes of TUM colors. TUM RGB-Dand RGB-D inputs. Tumexam. in. The TUM RGB-D dataset provides many sequences in dynamic indoor scenes with accurate ground-truth data. As an accurate 3D position track-ing technique for dynamic environment, our approach utilizing ob-servationality consistent CRFs can calculate high precision camera trajectory (red) closing to the ground truth (green) efficiently. Deep learning has promoted the. RGBD images. The experiments on the TUM RGB-D dataset [22] show that this method achieves perfect results. g. $ . de or mytum. I received my MSc in Informatics in the summer of 2019 at TUM and before that, my BSc in Informatics and Multimedia at the University of Augsburg. Estimating the camera trajectory from an RGB-D image stream: TODO. A robot equipped with a vision sensor uses the visual data provided by cameras to estimate the position and orientation of the robot with respect to its surroundings [11]. We provide examples to run the SLAM system in the KITTI dataset as stereo or monocular, in the TUM dataset as RGB-D or monocular, and in the EuRoC dataset as stereo or monocular. Registrar: RIPENCC Route: 131. Use directly pixel intensities!The feasibility of the proposed method was verified by testing the TUM RGB-D dataset and real scenarios using Ubuntu 18. 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. Configuration profiles. de TUM-RBG, DE. This table can be used to choose a color in WebPreferences of each web. net. rbg. TUM-Live, the livestreaming and VoD service of the Rechnerbetriebsgruppe at the department of informatics and mathematics at the Technical University of Munich. General Info Open in Search Geo: Germany (DE) — Domain: tum. Rainer Kümmerle, Bastian Steder, Christian Dornhege, Michael Ruhnke, Giorgio Grisetti, Cyrill Stachniss and Alexander Kleiner. rbg. [3] provided code and executables to evaluate global registration algorithms for 3D scene reconstruction system, and proposed the. The RGB-D case shows the keyframe poses estimated in sequence fr1 room from the TUM RGB-D Dataset [3], andWe provide examples to run the SLAM system in the TUM dataset as RGB-D or monocular, and in the KITTI dataset as stereo or monocular. de or mytum. Motchallenge. Digitally Addressable RGB. 2. Tracking ATE: Tab. tum. This is not shown. 55%. The TUM RGBD dataset [10] is a large set of data with sequences containing both RGB-D data and ground truth pose estimates from a motion capture system. The ICL-NUIM dataset aims at benchmarking RGB-D, Visual Odometry and SLAM algorithms. 73 and 2a09:80c0:2::73 . Network 131. support RGB-D sensors and pure localization on previously stored map, two required features for a significant proportion service robot applications. , ORB-SLAM [33]) and the state-of-the-art unsupervised single-view depth prediction network (i. From left to right: frame 1, 20 and 100 of the sequence fr3/walking xyz from TUM RGB-D [1] dataset. 73% improvements in high-dynamic scenarios. de which are continuously updated. Email: Confirm Email: Please enter a valid tum. VPN-Connection to the TUM set up of the RBG certificate Furthermore the helpdesk maintains two websites. md","contentType":"file"},{"name":"_download. You can change between the SLAM and Localization mode using the GUI of the map. 非线性因子恢复的视觉惯性建图。Mirror of the Basalt repository. Welcome to the RBG-Helpdesk! What kind of assistance do we offer? The Rechnerbetriebsgruppe (RBG) maintaines the infrastructure of the Faculties of Computer. 1illustrates the tracking performance of our method and the state-of-the-art methods on the Replica dataset. We also provide a ROS node to process live monocular, stereo or RGB-D streams. TUM RGB-D contains the color and depth images of real trajectories and provides acceleration data from a Kinect sensor. The sequences are from TUM RGB-D dataset. Contribution. A pose graph is a graph in which the nodes represent pose estimates and are connected by edges representing the relative poses between nodes with measurement uncertainty [23]. Please enter your tum. {"payload":{"allShortcutsEnabled":false,"fileTree":{"Examples/RGB-D":{"items":[{"name":"associations","path":"Examples/RGB-D/associations","contentType":"directory. de(PTR record of primary IP) IPv4: 131. tum. de credentials) Kontakt Rechnerbetriebsgruppe der Fakultäten Mathematik und Informatik Telefon: 18018. This file contains information about publicly available datasets suited for monocular, stereo, RGB-D and lidar SLAM. 89. The data was recorded at full frame rate (30 Hz) and sensor res-olution 640 480. The experiments are performed on the popular TUM RGB-D dataset . Cookies help us deliver our services. - GitHub - raulmur/evaluate_ate_scale: Modified tool of the TUM RGB-D dataset that automatically computes the optimal scale factor that aligns trajectory and groundtruth.