Simultaneous localization and mapping(SLAM)technology has made significant progress in ac-curacy and mapping,and has been widely used in areas such as home robotics and autonomous driving.With the rapid development of deep learning and neural networks,neural networks at this stage have the ability to learn universal laws from a huge amount of data,and can also be used as a new 3D representa-tion method.Based on this,the method of combining deep learning with SLAM technology has become a research hotspot.Recent advances in combining the SLAM techniques with deep learning-based image depth perception techniques is outlined,the latest approaches are summarized,and a feasible framework for building SLAM systems is proposed,where the depth perception techniques inside include depth esti-mation networks,neural radiance field(NeRF)and 3D Gaussian splatting(3DGS).The relationship be-tween these three depth perception techniques and their potential applications in SLAM are analysed in de-tail,offering a new perspective for the future development of SLAM and providing references for future studies.
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