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.
Visual odometry and SLAM datasets: The TUM RGB-D dataset @cite_9 is focused on the evaluation of RGB-D odometry and SLAM algorithms and has been extensively used by the research community. It provides 47 RGB-D sequences with ground-truth pose trajectories recorded with a motion capture system. It also comes with evaluation tools for measuring drift and SLAM trajectory alignment. For evaluating monocular odometry, recently the TUM MonoVO dataset @cite_0 has been proposed. The dataset contains 50 sequences in indoor and outdoor environments and has been photometrically calibrated for exposure times, lens vignetting and camera response function. Drift can be assessed by comparing the start and end position of the trajectory which coincide for the recordings. We also provide photometric calibration for our dataset, but additionally recorded motion capture ground truth in parts of the trajectories for better pose accuracy assessment. Furthermore, the above datasets do not include time-synchronized IMU measurements with the camera images like our benchmark.
@cite_0 @cite_9
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We introduce the Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset for the evaluation of visual odometry, 3D reconstruction and SLAM algorithms that typically use RGB-D data. We present a collection of handheld RGB-D camera sequences within synthetically generated environments. RGB-D sequences with perfect ground truth poses are provided as well as a ground truth surface model that enables a method of quantitatively evaluating the final map or surface reconstruction accuracy. Care has been taken to simulate typically observed real-world artefacts in the synthetic imagery by modelling sensor noise in both RGB and depth data. While this dataset is useful for the evaluation of visual odometry and SLAM trajectory estimation, our main focus is on providing a method to benchmark the surface reconstruction accuracy which to date has been missing in the RGB-D community despite the plethora of ground truth RGB-D datasets available. In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video frame rate (30 Hz). The ground-truth trajectory was obtained from a motion-capture system with eight high-speed tracking cameras (100 Hz). The dataset consists of 39 sequences that were recorded in an office environment and an industrial hall. The dataset covers a large variety of scenes and camera motions. We provide sequences for debugging with slow motions as well as longer trajectories with and without loop closures. Most sequences were recorded from a handheld Kinect with unconstrained 6-DOF motions but we also provide sequences from a Kinect mounted on a Pioneer 3 robot that was manually navigated through a cluttered indoor environment. To stimulate the comparison of different approaches, we provide automatic evaluation tools both for the evaluation of drift of visual odometry systems and the global pose error of SLAM systems. The benchmark website [1] contains all data, detailed descriptions of the scenes, specifications of the data formats, sample code, and evaluation tools. We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods. It contains 50 real-world sequences comprising more than 100 minutes of video, recorded across dozens of 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. We provide exposure times for each frame as reported by the sensor, the camera response function, and dense lens attenuation factors. We also propose a novel, simple approach to non-parametric vignette calibration, which requires minimal set-up and is easy to reproduce. Finally, we thoroughly evaluate two existing methods (ORB-SLAM and DSO) on the dataset, including an analysis of the effect of image resolution, camera field of view, and the camera motion direction. We propose a real-time depth edge based RGB-D SLAM system for dynamic environment. Our visual odometry method is based on frame-to-keyframe registration, where only depth edge points are used. To reduce the influence of dynamic objects, we propose a static weighting method for edge points in the keyframe. Static weight indicates the likelihood of one point being part of the static environment. This static weight is added into the Intensity Assisted Iterative Closest Point (IAICP) method to perform the registration task. Furthermore, our method is integrated into a SLAM (Simultaneous Localization and Mapping) system, where an efficient loop closure detection strategy is used. Both our visual odometry method and SLAM system are evaluated with challenging dynamic sequences from the TUM RGB-D dataset. Compared to state-of-the-art methods for dynamic environment, our method reduces the tracking error significantly.