Indoor Localization

Structured3D is a large-scale photo-realistic dataset containing 3.5K house designs (a) created by professional designers with a variety of ground truth 3D structure annotations (b) and generate photo-realistic 2D images (c). The dataset consists of rendering images and corresponding ground truth annotations (e.g., semantic, albedo, depth, surface normal, layout) under different lighting and furniture configurations. Source: https://github.com/bertjiazheng/Structured3D Image Source: https://github.com/bertjiazheng/Structured3D

Univesity is an indoor localization dataset proposed in "Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network". It contains camera images and ground-truth poses, split in 9694 training pairs and 5068 test pairs. It was created to test localization of multiple scenes in one global coordinate frame, and has a bigger indoor environment compared to 7scenes.

The Zillow Indoor Dataset (ZInD) provides extensive visual data that covers a real world distribution of unfurnished residential homes. It consists of primary 360º panoramas with annotated room layouts, windows, doors and openings (W/D/O), merged rooms, secondary localized panoramas, and final 2D floor plans. The figure above illustrates the various representations (from left to right beyond capture): Room layout with W/D/O annotations, merged layouts, 3D textured mesh, and final 2D floor plan.

IMU, WiFi data along with aligned Visual SLAM groundtruth locations from a smartphone carried during natural human motion

The Indoor-6 dataset was created from multiple sessions captured in six indoor scenes over multiple days. The pseudo ground truth (pGT) 3D point clouds and camera poses for each scene are computed using COLMAP. All training data uses only colmap reconstruction from training images. Compared to 7-scenes, the scenes in Indoor-6 are larger, have multiple rooms, contains illumination variations as the images span multiple days and different times of day.

Dataset containing channel state information (CSI) alongside ground truth data (position tags, timestamps) of a massive MIMO-OFDM system measured with the DICHASUS channel sounder. Measurement parameters and machine-readable file format descriptions are provided in a JSON file (spec.json). Distributed antenna setup with line-of-sight (LoS) and non-line-of-sight (NLoS) channels, measured in the ARENA2036 research factory campus environment.

Fusion-DHL is a multimodal sensor dataset with ground-truth positions.

The Lund University Vision, Radio, and Audio (LuViRA) positioning dataset consists of 89 trajectories that are recorded in the Lund University Humanities Lab's Motion Capture (Mocap) Studio using a MIR200 robot as the targeted platform. Each trajectory contains data from four different systems, vision, radio, audio and a ground truth system that can provide within 0.5mm localization accuracy. A Motion Capture (Mocap) system in the environment is used as the ground truth system, which provides 3D or 6DoF tracking of a camera, a single antenna and a speaker. These targets are mounted on top of the MIR200 robot and put in motion. 3D positions of the 11 static microphones are also provided.

The RISE (Robust Indoor Localization in Complex Scenarios) dataset is meant to train and evaluate visual indoor place recognizers. It contains more than 1 million geo-referenced images spread over 30 sequences, covering 5 heterogeneous buildings. For each building we provide: - A high resolution 3D point cloud (1cm) that defines the localization reference frame and that was generated with a mobile laser scanner and an inertial system. - Several image sequences spread over time with accurate ground truth poses retrieved by the laser scanner. Each sequence contains both, stereo pairs and spherical images. - Geo-referenced smartphone data, retrieved from the standard sensors of such devices.

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).

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.

A real world dataset for benchmarking global localization in complex indoor environments.

A ProcTHOR created synthetic dataset for benchmarking global localization in complex indoor environments.

This package contains an anonymized packets of 802.11 probe requests captured throughout March of 2023 at Universitat Jaume I. The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package). The dataset is usable for analyzis of Wi-Fi probe requests, presence detection, occupancy estimation or signal stability analyzis.