  Continual learning is essential for all real-world applications, as frozen
pre-trained models cannot effectively deal with non-stationary data
distributions. The purpose of this study is to review the state-of-the-art
methods that allow continuous learning of computational models over time. We
primarily focus on the learning algorithms that perform continuous learning in
an online fashion from considerably large (or infinite) sequential data and
require substantially low computational and memory resources. We critically
analyze the key challenges associated with continual learning for autonomous
real-world systems and compare current methods in terms of computations,
memory, and network/model complexity. We also briefly describe the
implementations of continuous learning algorithms under three main autonomous
systems, i.e., self-driving vehicles, unmanned aerial vehicles, and urban
robots. The learning methods of these autonomous systems and their strengths
and limitations are extensively explored in this article.
