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.

RollingShutter Modelling for Direct VisualInertial Odometry We present a direct visualinertial odometry (VIO) method which estimates the motion of the sensor setup and sparse 3D geometry of the environment based on measurements from a rollingshutter camera and an inertial measurement unit (IMU). The visual part of the system performs a photometric bundle adjustment on a sparse set of points. This direct approach does not extract feature points and is able to track not only corners, but any pixels with sufficient gradient magnitude. Neglecting rollingshutter effects in the visual part severely degrades accuracy and robustness of the system. In this paper, we incorporate a rollingshutter model into the photometric bundle adjustment that estimates a set of recent keyframe poses and the inverse depth of a sparse set of points. IMU information is accumulated between several frames using measurement preintegration, and is inserted into the optimization as an additional constraint between selected keyframes. For every keyframe we estimate not only the pose but also velocity and biases to correct the IMU measurements. Unlike systems with globalshutter cameras, we use both IMU measurements and rollingshutter effects of the camera to estimate velocity and biases for every state. Last, we evaluate our system on a new dataset that contains globalshutter and rollingshutter images, IMU data and groundtruth poses for ten different sequences, which we make publicly available. Evaluation shows that the proposed method outperforms a system where rolling shutter is not modelled and achieves similar accuracy to the globalshutter method on globalshutter data.