The field of autonomous navigation and sensing is witnessing significant advancements, driven by innovative approaches to address long-standing challenges. A key direction is the integration of multimodal sensing and machine learning techniques to enhance robustness and accuracy in various environments. Researchers are exploring the use of foundation models, transformer-based architectures, and sensor fusion methods to improve place recognition, loop closure detection, and obstacle detection. Another area of focus is the development of low-cost, configurable platforms for multi-agent autonomy research and underwater exploration. Noteworthy papers include: Multi-modal Loop Closure Detection with Foundation Models, which presents a multimodal pipeline for robust loop closure detection in severely unstructured environments. LODESTAR: Degeneracy-Aware LiDAR-Inertial Odometry, which introduces a novel method for addressing degeneracies in LiDAR-inertial odometry. HOTFLoc++: End-to-End Hierarchical LiDAR Place Recognition, which presents an end-to-end framework for LiDAR place recognition and 6-DoF metric localization in forests.