  Progress in the last decade has brought about significant improvements in the
accuracy and speed of SLAM systems, broadening their mapping capabilities.
Despite these advancements, long-term operation remains a major challenge,
primarily due to the wide spectrum of perturbations robotic systems may
encounter.
  Increasing the robustness of SLAM algorithms is an ongoing effort, however it
usually addresses a specific perturbation. Generalisation of robustness across
a large variety of challenging scenarios is not well-studied nor understood.
This paper presents a systematic evaluation of the robustness of open-source
state-of-the-art SLAM algorithms with respect to challenging conditions such as
fast motion, non-uniform illumination, and dynamic scenes. The experiments are
performed with perturbations present both independently of each other, as well
as in combination in long-term deployment settings in unconstrained
environments (lifelong operation).
