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
iMatching: Imperative Correspondence LearningCode1
Dense Visual Odometry Using Genetic Algorithm—0
Inertial Guided Uncertainty Estimation of Feature Correspondence in Visual-Inertial Odometry/SLAM—0
Resilient Simultaneous Localization and Mapping Fusing Ultra Wide Band Range Measurements and Visual Odometry—0
Converting Depth Images and Point Clouds for Feature-based Pose EstimationCode1
Jointly Optimized Global-Local Visual Localization of UAVs—0
XVO: Generalized Visual Odometry via Cross-Modal Self-Training—0
ObVi-SLAM: Long-Term Object-Visual SLAM—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration—0
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Benchmark Results

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