LiDAR and visual sensors play crucial roles in robot navigation by providing complementary environmental information. LiDAR sensors offer accurate 3D point cloud data, while visual sensors capture rich visual information. Robots can achieve robust and precise localization and mapping capabilities by fusing data from these sensors in indoor environments. This review discusses various sensor fusion approaches, including feature-based, direct, and probabilistic fusion methods. We examine the advantages and limitations of each approach and highlight recent advancements in the field. Furthermore, we discuss challenges and open research directions in sensor fusion for LiDAR and visual SLAM, such as handling dynamic environments, robustness to lighting conditions, and real-time performance requirements. This review aims to provide insights into the state-of-the-art techniques and inspire future research in sensor fusion for robot indoor navigation.