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 341–350 of 408 papers

TitleStatusHype
Fast Cylinder and Plane Extraction from Depth Cameras for Visual OdometryCode0
Learning monocular visual odometry with dense 3D mapping from dense 3D flow—0
SalientDSO: Bringing Attention to Direct Sparse OdometryCode0
Global Pose Estimation with an Attention-based Recurrent Network—0
LoopSmart: Smart Visual SLAM Through Surface Loop Closure—0
Learning Depth from Monocular Videos using Direct MethodsCode0
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments—0
Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM—0
Visual Odometry for Pixel Processor Arrays—0
Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields—0
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Benchmark Results

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