Convolutional Autoencoder aided loop closure detection for monocular SLAM
Convolutional Autoencoder aided loop closure detection for monocular SLAM is a scholarly work, published in 2018 in ''IFAC Proceedings Volumes''. The main subjects of the publication include closure, mobile robot, feature engineering, Simultaneous localization and mapping, artificial intelligence, computer vision, robot, loop, process, closing, indoor positioning system, base rate fallacy, and computer science. Tests conducted on the KITTI and the Scott Reef 25 dataset show that when bag-of-words approaches perform poorly, the authors' presented approach is able to avoid wrong loop closures.