You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Visual odometry and SLAM methods have a large variety of applications in domains such as augmented reality or robotics. Complementing vision sensors with inertial measurements tremendously improves tracking accuracy and robustness, and thus has spawned large interest in the development of visual-inertial (VI) odometry approaches. In this paper, we propose the TUM VI benchmark, a novel dataset with a diverse set of sequences in different scenes for evaluating VI odometry. It provides camera images with 1024x1024 resolution at 20 Hz, high dynamic range and photometric calibration. An IMU measures accelerations and angular velocities on 3 axes at 200 Hz, while the cameras and IMU sensors are time-synchronized in hardware. For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements. The full dataset with raw and calibrated data is publicly available. We also evaluate state-of-the-art VI odometry approaches on our dataset.

Cited Abstract(s):
[1]: This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i.e. 5–15 m above the ground). The 2 km dataset consists of time synchronized aerial high-resolution images, global position system and inertial measurement unit sensor data, ground-level street view images, and ground truth data. The dataset is ideal to evaluate and benchmark appearance-based localization, monocular visual odometry, simultaneous localization and mapping, and online three-dimensional reconstruction algorithms for micro aerial vehicles in urban environments.

[2]: We present PennCOSYVIO, a new challenging Visual Inertial Odometry (VIO) benchmark with synchronized data from a VI-sensor (stereo camera and IMU), two Project Tango hand-held devices, and three GoPro Hero 4 cameras. Recorded at UPenn's Singh center, the 150m long path of the hand-held rig crosses from outdoors to indoors and includes rapid rotations, thereby testing the abilities of VIO and Simultaneous Localization and Mapping (SLAM) algorithms to handle changes in lighting, different textures, repetitive structures, and large glass surfaces. All sensors are synchronized and intrinsically and extrinsically calibrated. We demonstrate the accuracy with which ground-truth poses can be obtained via optic localization off of fiducial markers. The data set can be found at https: daniilidis-group.github.io penncosyvio .

[3]: This paper presents visual-inertial datasets collected on-board a micro aerial vehicle. The datasets contain synchronized stereo images, IMU measurements and accurate ground truth. The first batch of datasets facilitates the design and evaluation of visual-inertial localization algorithms on real flight data. It was collected in an industrial environment and contains millimeter accurate position ground truth from a laser tracking system. The second batch of datasets is aimed at precise 3D environment reconstruction and was recorded in a room equipped with a motion capture system. The datasets contain 6D pose ground truth and a detailed 3D scan of the environment. Eleven datasets are provided in total, ranging from slow flights under good visual conditions to dynamic flights with motion blur and poor illumination, enabling researchers to thoroughly test and evaluate their algorithms. All datasets contain raw sensor measurements, spatio-temporally aligned sensor data and ground truth, extrinsic and intrinsic calibrations and datasets for custom calibrations.
Visual-inertial odometry and SLAM datasets: Similar to our benchmark, some recent datasets also provide time-synchronized IMU measurements with visual data and have been designed for the evaluation of visual-inertial (VI) odometry and SLAM approaches. The EuRoC MAV dataset [3] includes 11 indoor sequences recorded with a Skybotix stereo VI sensor from a MAV. Accurate ground truth (approx. 1mm) is recorded using a laser tracker or a motion capture system. Compared to our benchmark, the sequences in EuRoC MAV are shorter and have less variety as they only contain recordings in one machine hall and one lab room. Furthermore, EuRoC MAV does not include a photometric calibration which is important to benchmark direct methods. Further datasets for visual-inertial SLAM are the PennCOSYVIO dataset [2] and the Zurich Urban MAV dataset [1] . However, they do not contain photometric calibration and as accurate ground truth or time-synchronization of IMU and camera images like our benchmark (cf. tab:relateddatasets ).