The field of autonomous navigation and control is rapidly advancing, with a focus on developing innovative solutions for complex environments. Recent developments have emphasized the importance of accurate sensing, robust control, and efficient learning algorithms.

Notable advancements include the development of lightweight approaches for online slip detection and friction coefficient estimation, enabling real-time monitoring and control in autonomous driving. Additionally, end-to-end learning-based navigation systems have shown promise in addressing the challenges of reliable navigation in under-canopy agricultural environments.

The use of deep reinforcement learning policies and relative navigation frameworks has also demonstrated robust performance in urban environments and unstructured terrain. Furthermore, minimalistic autonomous stacks have been proposed for high-speed time-trial racing, emphasizing rapid deployment and efficient system integration.

Some noteworthy papers include:

• A lightweight approach for online slip detection and friction coefficient estimation, which achieves accurate and consistent results using IMU and LiDAR measurements.
• HUNT, a real-time framework that unifies traversal, acquisition, and tracking within a single relative formulation, demonstrating robust autonomy in dense forests and search-and-rescue operations.