•DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent CNNs
•UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
•VINet: Visual-Inertial Odometry as a Sequence-to-Sequence Learning Problem
•Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular DSO
•DeepTAM: Deep Tracking and Mapping
•Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning
•DeepFusion: Real-Time Dense 3D Reconstruction for Monocular SLAM using Single-View Depth and Gradient Predictions
•Training Deep SLAM on Single Frames
•DeepVIO: Self-supervised Deep Learning of Monocular VIO using 3D Geometric Constraints
•DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
•Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry
•Deep Direct Visual Odometry
•Learning By Inertia: Self-supervised Monocular VO For Road Vehicles