Monocular Visual Odometry
12 papers with code • 0 benchmarks • 3 datasets
These leaderboards are used to track progress in Monocular Visual Odometry
This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs).
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.
Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO).
EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner
The codes and the link for the dataset are publicly available at https://github. com/CapsuleEndoscope/EndoSLAM.
Many applications of Visual SLAM, such as augmented reality, virtual reality, robotics or autonomous driving, require versatile, robust and precise solutions, most often with real-time capability.
However, standard visual odometry or SLAM algorithms require motion parallax to initialize (see Figure 1) and, therefore, suffer from delayed initialization.