Monocular Visual Odometry
18 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Monocular Visual Odometry
Datasets
TUM monoVO
EndoSLAM
ConsInv Dataset
Drunkard's Dataset
Bike and Car Odometer Dataset ! Speedometer OCR
Latest papers with no code
Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning
Deep learning algorithms have driven expressive progress in many complex tasks.
NeRF-VO: Real-Time Sparse Visual Odometry with Neural Radiance Fields
We introduce a novel monocular visual odometry (VO) system, NeRF-VO, that integrates learning-based sparse visual odometry for low-latency camera tracking and a neural radiance scene representation for sophisticated dense reconstruction and novel view synthesis.
XVO: Generalized Visual Odometry via Cross-Modal Self-Training
We propose XVO, a semi-supervised learning method for training generalized monocular Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and settings.
Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles using Feedforward Neural Networks
The drift reducing neural network identifies such errors based on the motion of features in the successive camera frames leading to more accurate incremental motion estimates.
Event-aided Direct Sparse Odometry
This opens the door to low-power motion-tracking applications where frames are sparingly triggered "on demand" and our method tracks the motion in between.
Improving Monocular Visual Odometry Using Learned Depth
The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes.
RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry
To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol.
Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World
In this work, we propose VRVO, a novel framework for retrieving the absolute scale from virtual data that can be easily obtained from modern simulation environments, whereas in the real domain no stereo or ground-truth data are required in either the training or inference phases.
A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation
This paper proposes a high-precision self-supervised monocular VO, which is specifically designed for navigation in foggy weather.
Deep Online Correction for Monocular Visual Odometry
Second, the poses predicted by CNNs are further improved by minimizing photometric errors via gradient updates of poses during inference phases.
