NEAR: The NetEase AR Oriented Visual Inertial Dataset The existing datasets for evaluating Visual Inertial Odometry (VIO) have boosted the research of autonomous agents, but they donu0027t meet the prosperous research of Augmented Reality (AR) or Mixed Reality (MR) given that they are not collected at real AR scenes and do not account for affecting factors of mobile devices. This paper presents the NEAR dataset, an AR oriented visualinertial dataset collected with commodity handheld phones with ground truth. The dataset has a total of 110 sequences in 49 elaborately designed collection cases at two typical indoor scenes, i.e. the living area and the table area. It also covers plenty of setting adjustments for comparison, including the comparisons of different level textures, illuminations, motion patterns, camera settings and the difference between the rolling shutter and the global shutter. The full dataset along with the calibration data has been publicly available.

A Benchmark for VisualInertial Odometry Systems Employing Onboard Illumination We present a dataset for evaluating the performance of visualinertial odometry (VIO) systems employing an onboard light source. The dataset consists of 39 sequences, recorded in mines, tunnels, and other dark environments, totaling more than 160 minutes of stereo camera video and IMU data. In each sequence, the scene is illuminated by an onboard light of approximately 1300, 4500, or 9000 lumens. We accommodate both direct and indirect visual odometry methods by providing the geometric and photometric camera calibrations (i.e. response, attenuation, and exposure times). In contrast with existing datasets, we also calibrate the light source itself and publish data for inferring more complex light models. Groundtruth position data are available for a subset of sequences, as captured by a Leica total station. All remaining sequences start and end at the same position, permitting the use of total accumulated drift as a metric for evaluation. Using our proposed benchmark, we analyze the performance of several startoftheart VO and VIO frameworks. The full dataset, including sensor data, calibration sequences, and evaluation scripts, is publicly available online at http:arpg.colorado.eduresearchoivio.