Monocular Visual Odometry

18 papers with code • 0 benchmarks • 5 datasets

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Most implemented papers

DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks

ChiWeiHsiao/DeepVO-pytorch 25 Sep 2017

This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs).

Visual Odometry Revisited: What Should Be Learnt?

Huangying-Zhan/DF-VO 21 Sep 2019

In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning.

DF-VO: What Should Be Learnt for Visual Odometry?

Huangying-Zhan/DF-VO 1 Mar 2021

More surprisingly, they show that the well-trained networks enable scale-consistent predictions over long videos, while the accuracy is still inferior to traditional methods because of ignoring geometric information.

Unsupervised Scale-consistent Depth Learning from Video

JiawangBian/sc_depth_pl 25 May 2021

We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.

Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes

hkashyap/SparseMotion 9 Mar 2019

Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO).

Extending Monocular Visual Odometry to Stereo Camera Systems by Scale Optimization

jiawei-mo/scale_optimization 29 May 2019

This paper proposes a novel approach for extending monocular visual odometry to a stereo camera system.

EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner

CapsuleEndoscope/EndoSLAM 30 Jun 2020

The codes and the link for the dataset are publicly available at https://github. com/CapsuleEndoscope/EndoSLAM.

WGANVO: Monocular Visual Odometry based on Generative Adversarial Networks

CIFASIS/wganvo 27 Jul 2020

In this work we present WGANVO, a Deep Learning based monocular Visual Odometry method.

OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications

ov2slam/ov2slam 8 Feb 2021

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.

Instant Visual Odometry Initialization for Mobile AR

facebookresearch/relative_pose_dataset 30 Jul 2021

However, standard visual odometry or SLAM algorithms require motion parallax to initialize (see Figure 1) and, therefore, suffer from delayed initialization.