The field of robotic navigation and localization is rapidly advancing, driven by the need for more accurate and efficient methods for navigating complex environments. Recent research has focused on developing new approaches that leverage advances in sensor technology, machine learning, and computer vision to improve the accuracy and robustness of navigation systems. One key direction is the use of novel sensor modalities, such as radar and lidar, to provide more accurate and reliable sensing in a variety of environments. Another area of focus is the development of new algorithms and techniques for fusing data from multiple sensors and sources to improve navigation accuracy. Notable papers in this area include: PC-DeepNet, which presents a novel learning-based framework for GNSS positioning error minimization. RadarTrack, which introduces a innovative ego-speed estimation framework utilizing a single-chip mmWave radar. Long Exposure Localization in Darkness Using Consumer Cameras, which evaluates the performance of the SeqSLAM algorithm for passive vision-based localization in very dark environments.