Comparison of modern open-source visual SLAM approaches
This work provides a comparative analysis for SLAM researchers, but it is incremental as it focuses on benchmarking existing methods.
The paper tackled the problem of comparing modern open-source visual SLAM approaches by analyzing their accuracy, computational performance, robustness, and fault tolerance on common datasets, finding that it raises crucial questions for researchers.
SLAM is one of the most fundamental areas of research in robotics and computer vision. State of the art solutions has advanced significantly in terms of accuracy and stability. Unfortunately, not all the approaches are available as open-source solutions and free to use. The results of some of them are difficult to reproduce, and there is a lack of comparison on common datasets. In our work, we make a comparative analysis of state of the art open-source methods. We assess the algorithms based on accuracy, computational performance, robustness, and fault tolerance. Moreover, we present a comparison of datasets as well as an analysis of algorithms from a practical point of view. The findings of the work raise several crucial questions for SLAM researchers.