Object SLAM

The YCB-Slide dataset comprises of DIGIT sliding interactions on YCB objects. We envision this can contribute towards efforts in tactile localization, mapping, object understanding, and learning dynamics models. We provide access to DIGIT images, sensor poses, RGB video feed, ground-truth mesh models, and ground-truth heightmaps + contact masks (simulation only). This dataset is supplementary to the MidasTouch paper, a CoRL 2022 submission.

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).

TorWIC is the dataset discussed in POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes. The purpose of this dataset is to evaluate the map mainteneance capabilities in a warehouse environment undergoing incremental changes. This dataset is collected in a Clearpath Robotics facility.

The Cooperative Driving dataset is a synthetic dataset generated using CARLA that contains lidar data from multiple vehicles navigating simultaneously through a diverse set of driving scenarios. This dataset was created to enable further research in multi-agent perception (cooperative perception) including cooperative 3D object detection, cooperative object tracking, multi-agent SLAM and point cloud registration. Towards that goal, all the frames have been labelled with ground-truth sensor pose and 3D object bounding boxes.

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.