Imparting human-like commonsense to machines is a long-term goal in the artificial intelligence community. To achieve this goal, constructing large-scale commonsense knowledge resources is an important step. In recent years, due to increasing demand, commonsense knowledge has become a rapidly growing research field, resulting in a surge of new acquisition methods and corresponding resources. These advances have empowered a variety of downstream AI tasks. However, constructing large-scale commonsense knowledge resources remains an ongoing and challenging task. It is still difficult to efficiently collect large-scale, high-quality commonsense knowledge. In this paper, we systematically review recent advances in commonsense knowledge acquisition methods and resources, providing a comprehensive summary of recent research scope, the characteristics of different resources, and unsolved challenges.