The recent advancements in mobile robotics have significantly enhanced the capabilities of autonomous systems, particularly in the areas of environment prediction, roadmap construction, and topological mapping. Researchers are increasingly leveraging learning-based methods to enable robots to predict and navigate complex, unknown environments more efficiently. These methods often combine direct observations with predictive models to improve navigation safety and efficiency. Additionally, the development of novel roadmap construction techniques, such as those based on reaction diffusion systems, has led to more robust and query-efficient path planning solutions. Topological mapping approaches are also gaining traction due to their reduced resource consumption and resilience to odometry errors, offering a promising alternative to traditional metric mapping methods. Furthermore, the ability to update dynamic maps in real-time is advancing the field of human-aware robot navigation and localization, ensuring that robots can adapt to changing environments effectively.

Noteworthy papers include one that introduces a learning-based approach for predicting 3D object shapes from partial views, significantly enhancing next-best-view planning, and another that proposes a novel roadmap construction method using a reaction diffusion system, which consistently outperforms classical methods in terms of connectivity and query efficiency.