Realtime 3D reconstruction algorithm based on improved AKAZE and multi-layer feature detection network

Abstract

Simultaneous localization and mapping (SLAM) technology aims to solve the problem of how to determine the trajectory of robots by observing the environment and construct sparse spatial models. And the 3D reconstruction technology can further build dense spatial models based on the pose information. This paper proposes a dense 3D reconstruction algorithm based on improved accelerated-KAZE features and a multi-layer feature detection network. A multi-layer feature detection neural network is constructed to replace the bag-of-words model and optimize the loopback results, which improves the effect of pose estimation. The optimized algorithm improves the mapping effect and construct clear and robust indoor complex models. The pose estimation results show that the optimized algorithm improves the accuracy by 30% compared with ORB-SLAM2. Compared with the mapping effect of Kintinuous, ElasticFusion, ORB-SLAM2 and BundleFusion, the optimized models own the advantages of clearer details, richer information and no split layer.