Open Dataset for Testing of Visual SLAM Algorithms under Different Weather Conditions

Abstract

Existing datasets for testing SLAM algorithms in outdoor environments are not suitable for assessing the influence of weather conditions on localization accuracy. Obtaining a suitable dataset from the real world is difficult due to the long data collection period and the inability to exclude dynamic environmental factors. Artificially generated datasets make it possible to bypass the described limitations, but up to date, researchers have not identified testing SLAM algorithms under different weather conditions as a stand-alone task, despite the fact that it is one of the main aspects of the difference between outdoor and indoor environments. This work presents a new open dataset that consists of 36 sequences of robot movement in an urban environment or rough terrain, in the form of images from a stereo camera and the ground truth position of the robot, collected at a frequency of 30 Hz. Movement within one area occurs along a fixed route; the sequences are distinguished only by whether conditions, which can make it possible to correctly assess the influence of weather phenomena on the accuracy of localization.