  Simultaneous Localization and Mapping (SLAM) plays an important role in
outdoor and indoor applications ranging from autonomous driving to indoor
robotics. Outdoor SLAM has been widely used with the assistance of LiDAR or
GPS. For indoor applications, the LiDAR technique does not satisfy the accuracy
requirement and the GPS signals will be lost. An accurate and efficient scene
sensing technique is required for indoor SLAM. As the most promising 3D sensing
technique, the opportunities for indoor SLAM with fringe projection
profilometry (FPP) systems are obvious, but methods to date have not fully
leveraged the accuracy and speed of sensing that such systems offer. In this
paper, we propose a novel FPP-based indoor SLAM method based on the coordinate
transformation relationship of FPP, where the 2D-to-3D descriptor-assisted is
used for mapping and localization. The correspondences generated by matching
descriptors are used for fast and accurate mapping, and the transform
estimation between the 2D and 3D descriptors is used to localize the sensor.
The provided experimental results demonstrate that the proposed indoor SLAM can
achieve the localization and mapping accuracy around one millimeter.
