SOTAVerified

Visual Odometry

Visual Odometry is an important area of information fusion in which the central aim is to estimate the pose of a robot using data collected by visual sensors.

Source: Bi-objective Optimization for Robust RGB-D Visual Odometry

Papers

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Showing 271–280 of 408 papers

TitleStatusHype
Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular VideoCode1
Improving Visual Feature Extraction in Glacial Environments—0
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
Multi-Spectral Visual Odometry without Explicit Stereo Matching—0
Some Aspects of Geometric Computer Vision for Analysing Dynamical Scenes focusing Automotive Applications—0
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction—0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Local Supports Global: Deep Camera Relocalization with Sequence Enhancement—0
Unsupervised Learning of Depth and Deep Representation for Visual Odometry from Monocular Videos in a Metric Space—0
Distilling Knowledge From a Deep Pose Regressor Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CIVORelative Position Error Translation [cm]1.36—Unverified