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 191–200 of 408 papers

TitleStatusHype
GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method—0
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video—0
Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment—0
MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge Intelligence—0
MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry—0
Robust Edge-Direct Visual Odometry based on CNN edge detection and Shi-Tomasi corner optimization—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
LOF: Structure-Aware Line Tracking based on Optical Flow—0
Recalibrating the KITTI Dataset Camera Setup for Improved Odometry AccuracyCode0
The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation—0
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Benchmark Results

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