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 281–290 of 408 papers

TitleStatusHype
Robust Real-time RGB-D Visual Odometry in Dynamic Environments via Rigid Motion Model—0
Scene Motion Decomposition for Learnable Visual Odometry—0
Stereo relative pose from line and point feature tripletsCode0
SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks—0
PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning—0
Edge-Direct Visual OdometryCode0
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth—0
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching VideosCode1
Extending Monocular Visual Odometry to Stereo Camera Systems by Scale OptimizationCode0
Robust Dense Mapping for Large-Scale Dynamic Environments—0
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Benchmark Results

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