The field of autonomous navigation and mapping is rapidly evolving, with a focus on developing more efficient and effective methods for robots to understand and interact with their environments. Recent research has emphasized the importance of exploiting prior knowledge and experience to improve navigation and mapping performance. This includes the use of retrieval-augmented agents, semantic guided exploration, and path database guidance to enable robots to learn from their past experiences and adapt to new situations. Additionally, there is a growing interest in developing lightweight and efficient neural network-based approaches for indoor exploration and mapping. Noteworthy papers in this area include RANa, which introduces a retrieval-augmented agent for navigation, and SeGuE, which develops a semantic guided exploration method for mobile robots. Other notable papers, such as Mapping at First Sense and RayFronts, propose innovative approaches for indoor mapping and online scene understanding, respectively.