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 81–90 of 408 papers

TitleStatusHype
A Photometrically Calibrated Benchmark For Monocular Visual Odometry—0
AFT-VO: Asynchronous Fusion Transformers for Multi-View Visual Odometry Estimation—0
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video—0
DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
Computing Similarity Transformations From Only Image Correspondences—0
A Novel Georeferenced Dataset for Stereo Visual Odometry—0
CodedVO: Coded Visual Odometry—0
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map—0
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving—0
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Benchmark Results

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