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
Deep Event Visual Odometry
tum-vision/devo
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To remove the dependency on additional sensors and to push the limits of using only a single event camera, we present Deep Event VO (DEVO), the first monocular event-only system with strong performance on a large number of real-world benchmarks.
Monocular visual simultaneous localization and mapping:(r) evolution from geometry to deep learning-based pipelines
With the rise of deep learning, there is a fundamental change in visual simultaneous localization and mapping (SLAM) algorithms toward developing different modules trained as end-to-end pipelines.
Transformer-based model for monocular visual odometry: a video understanding approach
In this work, we deal with the monocular visual odometry as a video understanding task to estimate the 6-DoF camera's pose.
Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry
Monocular visual odometry consists of the estimation of the position of an agent through images of a single camera, and it is applied in autonomous vehicles, medical robots, and augmented reality.
Deep Patch Visual Odometry
princeton-vl/dpvo
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NeurIPS 2023
DPVO disproves this assumption, showing that it is possible to get the best accuracy and efficiency by exploiting the advantages of sparse patch-based matching over dense flow.
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo
In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework.
MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints
A key aspect of our approach is to use an appropriate motion model that can help existing self-supervised monocular VO (SSM-VO) algorithms to overcome issues related to the local minima within their self-supervised loss functions.
Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions
In contrast to most other approaches, our framework can also handle rotation-only motions that are particularly challenging for monocular odometry systems.
Instant Visual Odometry Initialization for Mobile AR
However, standard visual odometry or SLAM algorithms require motion parallax to initialize (see Figure 1) and, therefore, suffer from delayed initialization.
Unsupervised Scale-consistent Depth Learning from Video
JiawangBian/SC-SfMLearner-Release
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We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.
