Deep Visual Inertial Odometry with Kalman Filter скачать в хорошем качестве

Deep Visual Inertial Odometry with Kalman Filter

Explore the advanced integration of deep learning in visual-inertial odometry in our comprehensive GitHub repository. This project highlights the development of a lightweight convolutional neural network architecture. It emphasizes the use of exponential maps and Lie groups to refine the training of neural networks with visual and inertial data, enhancing frame-to-frame navigation accuracy without relying on traditional RNNs. Dive deep into our system's technical details and practical applications in real-world autonomous navigation scenarios. ----------------------------------------------------------------------------------- 📝In-video sources ➡️ Git of Deep V-I-O: https://github.com/ElliotHYLee/Deep_V... ➡️ Git of Deep VO: https://github.com/ChiWeiHsiao/DeepVO... ----------------------------------------------------------------------------------- 🌏 Find Me Here: 🔥Linkedin:   / hongyun-elliot-lee   🔥Discord:   / discord   🔥ubicoders: https://www.ubicoders.com/ 🔥Blog: https://www.ubicoders.com/blogs 🔥GitHub: https://github.com/ubicoders/ ----------------------------------------------------------------------------------- ⏲️Time Stamps: 0:00 intro 1:17 camera calibration - traditional visual odometry pipeline 2:13 feature matching and extraction - traditional visual odometry pipeline 3:33 3D point cloud - traditional visual odometry pipeline 5:00 computing camera rotation and translation - traditional visual odometry pipeline 6:07 optical flow 7:13 flownet - optical flow CNN 8:08 camera ego-motion estimation with deep learning 10:12 extra backgrounds needed for this project 13:53 semi-supervised learning with Mahalanobis distance 19:05 demo and results 25:00 is there any better approach? 26:06 Code requirements 29:58 quick demo file 33:14 plotting result-only demo 40:26 kalman filter wrapping demo 43:10 data readers explained 45:16 models explained #deeplearning #convolutionalneuralnetworks #computervision