The field of autonomous systems and climate monitoring is moving towards increased accuracy and reliability in various applications such as radar echogram layer tracking, teach and repeat in off-road environments, and visual localization. Research is focusing on developing innovative solutions to overcome challenges posed by changing environments and harsh conditions. Notable advancements include the development of deep learning algorithms for radar echogram analysis and the creation of comprehensive datasets for testing and comparison. Additionally, there is a growing interest in exploring the limits of simpler algorithms, such as the direct integration of wheel encoder data and yaw rate measurements, which can provide accurate results with lower computational costs. Some papers are particularly noteworthy, including the introduction of the AI-ready Snow Radar Echogram Dataset for climate change monitoring, and the proposal of LiftFeat, a new lightweight network for 3D geometry-aware local feature matching.