The chapter "Semantic Scene Segmentation for Robotics" by Juana Valeria Hurtado and Abhinav Valada provides a comprehensive overview of semantic scene segmentation, a critical task for enabling robot autonomy. Semantic segmentation aims to assign a semantic class label to each pixel in an image, providing a dense prediction that captures the full scene context, including object categories, locations, and shapes. The authors discuss the importance of scene understanding in robotics, highlighting its role in tasks such as mapping, navigation, and interaction. They review the evolution of semantic segmentation techniques, from traditional methods using clustering and edge detection to deep learning approaches that have significantly advanced the field. Key deep learning methods, including Fully Convolutional Networks (FCNs) and encoder-decoder architectures, are detailed, along with their improvements and challenges. The chapter also covers loss functions, real-time architectures, and methods for handling multiple inputs, such as video, point clouds, and multimodal data. Additionally, it reviews popular datasets and benchmarks used in the field, emphasizing the role of large-scale labeled datasets in advancing semantic segmentation techniques. Finally, the chapter discusses the challenges and opportunities for further research in this area, underscoring the importance of semantic information for robotics applications.The chapter "Semantic Scene Segmentation for Robotics" by Juana Valeria Hurtado and Abhinav Valada provides a comprehensive overview of semantic scene segmentation, a critical task for enabling robot autonomy. Semantic segmentation aims to assign a semantic class label to each pixel in an image, providing a dense prediction that captures the full scene context, including object categories, locations, and shapes. The authors discuss the importance of scene understanding in robotics, highlighting its role in tasks such as mapping, navigation, and interaction. They review the evolution of semantic segmentation techniques, from traditional methods using clustering and edge detection to deep learning approaches that have significantly advanced the field. Key deep learning methods, including Fully Convolutional Networks (FCNs) and encoder-decoder architectures, are detailed, along with their improvements and challenges. The chapter also covers loss functions, real-time architectures, and methods for handling multiple inputs, such as video, point clouds, and multimodal data. Additionally, it reviews popular datasets and benchmarks used in the field, emphasizing the role of large-scale labeled datasets in advancing semantic segmentation techniques. Finally, the chapter discusses the challenges and opportunities for further research in this area, underscoring the importance of semantic information for robotics applications.