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 326–350 of 408 papers

TitleStatusHype
Deep Visual Odometry Methods for Mobile Robots—0
A Synchronized Stereo and Plenoptic Visual Odometry Dataset—0
The Double Sphere Camera ModelCode0
Dynamic Objects Segmentation for Visual Localization in Urban Environments—0
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry—0
Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments—0
SIPs: Succinct Interest Points from Unsupervised Inlierness Probability LearningCode0
Evolution of Visual Odometry Techniques—0
The TUM VI Benchmark for Evaluating Visual-Inertial OdometryCode1
FDMO: Feature Assisted Direct Monocular Odometry—0
Geometric Consistency for Self-Supervised End-to-End Visual Odometry—0
Evaluation of the visual odometry methods for semi-dense real-time—0
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature ReconstructionCode0
Deep Auxiliary Learning for Visual Localization and OdometryCode0
Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction—0
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