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 introduces a dataset gathered entirely in urban scenarios with a car equipped with one stereo camera and five laser scanners, among other sensors. One distinctive feature of the present dataset is the existence of high-resolution stereo images grabbed at a high rate (20 fps) during a 36.8 km trajectory, which allows the benchmarking of a variety of computer vision techniques. We describe the sensors employed and highlight some applications which could be benchmarked using the present work. Both plain text and binary files are provided, as well as open-source tools for working with the binary versions. The dataset is available for download at http: www.mrpt.org MalagaUrbanDataset.

[2]: We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. This resulted in over 1000 km of recorded driving with almost 20 million images collected from 6 cameras mounted to the vehicle, along with LIDAR, GPS and INS ground truth. Data was collected in all weather conditions, including heavy rain, night, direct sunlight and snow. Road and building works over the period of a year significantly changed sections of the route from the beginning to the end of data collection. By frequently traversing the same route over the period of a year we enable research investigating long-term localization and mapping for autonomous vehicles in real-world, dynamic urban environments. The full dataset is available for download at: http: robotcar-dataset.robots.ox.ac.uk

[3]: We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.
For research on autonomous driving, visual odometry and SLAM datasets have been proposed such as Kitti [3] , Malaga Urban dataset [1] , or the Robot Oxford car dataset [2] . The Kitti and Malaga Urban datasets also include low-frequency IMU information which is, however, not time-synchronized with the camera images. While Kitti provides a GPS INS-based ground truth with accuracy below 10 ,cm, the Malaga Urban dataset only includes a coarse position for reference from a low-cost GPS sensor. Our dataset contains 20 camera images and hardware time-synchronized 3-axis accelerometer and gyro measurements at 200 . Ground-truth poses are recorded at 120 and are accurately time-aligned with the sensor measurements as well.