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Synthetic dataset for navigation tasks of autonomous systems and ground robots

Modification and improvement of visual odometry algorithms are essential for the successful and stable functioning of autonomous systems and robots. Existing real datasets are not well scalable and cover a limited set of scenarios and motion models in comparison with real cases. The provision of a new large volume of annotated data that is solved by obtaining the synthetic data using a computer simulation is an urgent problem. Such synthetic datasets have the advantage of being better scalable. The paper presents a large-scale synthetic dataset of indoor and outdoor video sequences for ground autonomous systems and robot navigation tasks. The main characteristics of our dataset are a high degree of realism and variability, simulation of lighting changes, presence of moving objects in virtual scene, as well as providing different types of trajectories for the movement of a ground robot. As a result, the direct visual odometry algorithm was tested on the created synthetic dataset.