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We present a dataset for evaluating the tracking accuracy of monocular visualodometry and SLAM methods. It contains 50 real-world sequences comprising morethan 100 minutes of video, recorded across dozens of different environments --ranging from narrow indoor corridors to wide outdoor scenes. All sequencescontain mostly exploring camera motion, starting and ending at the sameposition. This allows to evaluate tracking accuracy via the accumulated driftfrom start to end, without requiring ground truth for the full sequence. Incontrast to existing datasets, all sequences are photometrically calibrated. Weprovide exposure times for each frame as reported by the sensor, the cameraresponse function, and dense lens attenuation factors. We also propose a novel,simple approach to non-parametric vignette calibration, which requires minimalset-up and is easy to reproduce. Finally, we thoroughly evaluate two existingmethods (ORB-SLAM and DSO) on the dataset, including an analysis of the effectof image resolution, camera field of view, and the camera motion direction.

A Photometrically Calibrated Benchmark For Monocular Visual Odometry