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 221–230 of 408 papers

TitleStatusHype
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motionCode2
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching—0
Event-based Stereo Visual OdometryCode1
What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector—0
Deep Keypoint-Based Camera Pose Estimation with Geometric ConstraintsCode1
Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods—0
Robust Ego and Object 6-DoF Motion Estimation and TrackingCode1
WGANVO: Monocular Visual Odometry based on Generative Adversarial NetworksCode0
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling—0
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Benchmark Results

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