The field of autonomous systems is witnessing significant developments in robust perception systems, aiming to enhance the accuracy and reliability of semantic segmentation, object detection, and state estimation in challenging environments. Researchers are exploring innovative approaches, including data-centric methods, multi-sensor fusion, and robustness evaluation frameworks, to address the limitations of existing systems. Notably, the integration of diverse sensor modalities, such as LiDAR, polarization vision, inertial measurement units, and optical flow, is becoming increasingly popular to improve the robustness and accuracy of perception systems. Furthermore, the development of realistic synthetic data augmentation pipelines and benchmark datasets is facilitating the evaluation and improvement of robustness in autonomous vehicle camera radar datasets. Noteworthy papers include:

• A Data-Centric Approach to 3D Semantic Segmentation of Railway Scenes, which introduces targeted data augmentation methods to improve segmentation performance on railway-specific datasets.
• LPVIMO-SAM, a tightly-coupled LiDAR/Polarization Vision/Inertial/Magnetometer/Optical Flow Odometry via Smoothing and Mapping framework, enables high-precision and highly robust real-time state estimation and map construction in challenging environments.
• An Underwater, Fault-Tolerant, Laser-Aided Robotic Multi-Modal Dense SLAM System for Continuous Underwater In-Situ Observation, which presents a novel laser-aided multi-sensor fusion system capable of uninterrupted, fault-tolerant dense SLAM in diverse complex underwater scenarios.