The field of SLAM and autonomous navigation is moving towards more reliable and reproducible multimodal datasets, with a focus on open-hardware designs and robust calibration and synchronization pipelines. This allows for more accurate and efficient development and evaluation of SLAM algorithms. Meanwhile, innovative applications of deep learning and computer vision are being explored for early detection of visual impairments and automated dermatological diagnostics. These advances have the potential to improve treatment effectiveness and enable accessible screenings worldwide. Noteworthy papers include: SMapper, a novel open-hardware platform for SLAM research, which establishes a robust foundation for advancing SLAM algorithm development and evaluation. The KidsVisionCheck application, which provides highly reliable performance for pediatric vision screenings using red-eye reflex images and deep neural networks.