Monocular Visual Odometry

TUM monoVO is a dataset for evaluating the tracking accuracy of monocular Visual Odometry (VO) and SLAM methods. It contains 50 real-world sequences comprising over 100 minutes of video, recorded across different environments – ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting and ending at the same position: this allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground-truth for the full sequence. In contrast to existing datasets, all sequences are photometrically calibrated: the dataset creators provide the exposure times for each frame as reported by the sensor, the camera response function and the lens attenuation factors (vignetting). Source: https://vision.in.tum.de/data/datasets/mono-dataset Image Source: https://vision.in.tum.de/data/datasets/mono-dataset

The endoscopic SLAM dataset (EndoSLAM) is a dataset for depth estimation approach for endoscopic videos. It consists of both ex-vivo and synthetically generated data. The ex-vivo part of the dataset includes standard as well as capsule endoscopy recordings. The dataset is divided into 35 sub-datasets. Specifically, 18, 5 and 12 sub-datasets exist for colon, small intestine and stomach respectively. Source: https://github.com/CapsuleEndoscope/EndoSLAM Image Source: https://github.com/CapsuleEndoscope/EndoSLAM

This dataset presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data. We introduce a new benchmark targeting visual odometry and SLAM, to advance the research in autonomous robotics and computer vision. This work complements existing datasets by simultaneously addressing several issues, such as environment diversity, motion patterns, and sensor frequency. It uses up-to-date devices and presents effective procedures to accurately calibrate the intrinsic and extrinsic of the sensors while addressing temporal synchronization. During recording, we cover multi-floor buildings, gardens, urban and highway scenarios. Combining handheld and car-based data collections, our setup can simulate any robot (quadrupeds, quadrotors, autonomous vehicles). The dataset includes an accurate 6-dof ground truth based on a novel methodology that refines the RTK-GPS estimate with LiDAR point clouds through Bundle Adjustment. All sequences divided in training and testing are accessible through our website.

Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.

This dataset comprehends the 3D building information model (in IFC and Revit formats), manually elaborated based on the terrestrial laser scanner of the sequence 2 of ConSLAM, and the refined ground truth (GT) poses (in TUM format) of sessions 2, 3, 4, and 5 of the open-access ConSLAM dataset (which provides camera, LiDAR, and IMU measurements). This dataset enables the evaluation of pose estimation algorithms, such as localization, odometry, SLAM, and Bundle adjustment. Given the differences between the real-world and the 3D reference BIM model (Scan-Map deviations), the dataset is appropriate for evaluating long-term localization and mapping capabilities, i.e., methods that are robust to changes in the environment. In particular, in a real-world construction environment with normal clutter (not as in the Hilti dataset).

ConsInv is a stereo RGB + IMU dataset designed for Dynamic SLAM testing and contains two subsets: - ConsInv-Indoors contains sequences in an office setting where small objects are moved. - ConsInv-Outdoors contains sequences in an urban environment, where cars and/or people move. The novelty of ConsInv dataset is 1) the controlled degree of difficulty, from easy to very hard, and 2) the fact that the difficulty of the sequences comes only from object motion: relative motion between camera and object, motion ambiguity, challenging points of view when objects move. The difficulty does not come from motion speed, lack of features, lens flare, etc. - typically seen in other SLAM datasets.

ConSLAM is a real-world dataset collected periodically on a construction site to measure the accuracy of mobile scanners' SLAM algorithms. The dataset contains time-synchronized and spatially registered RGB and NIR images and 360-deg LiDAR scans, 9-axis IMU measurements, and professional ground-truth terrestrial laser scans. This dataset reflects the periodic need to scan construction sites with the aim of accurately monitoring progress using a hand-held scanner. The sensors used for data acquisition are: - LiDAR: Velodyne VLP-16. - RGB camera: Alvium U-319c, 3.2 MP. - NIR camera: Alvium 1800 U-501, 5.0 MP. - 9-axis IMU: Xsens MTi-610.

This dataset consists of odometer or speedometer images of bike and car vehicles. ### Introduction This dataset can be used to detect or recognize odometer readings of the vehicles. Moreover, it can be used to classify the make of the car and bikes. The usecases can in the domain of insurance, repair and OCR. ### Dataset Features - Captured by 4000+ unique users - Rich in diversity - Mobile phone view point - Various lighting conditions - Digital and Analog Categories - Vehicle Model Types ### Dataset Features - Classification and detection annotations available - Multiple category annotations possible - COCO, PASCAL VOC and YOLO formats To download full datasets or to submit a request for your dataset needs, please ping us on sales@datacluster.ai Visit www.datacluster.in to know more. Note: All the images are manually verified and are contributed by the large contributor base on DataCluster platform