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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–375 of 408 papers

TitleStatusHype
DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural NetworksCode0
Playing for Benchmarks—0
UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning—0
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation—0
DPC-Net: Deep Pose Correction for Visual LocalizationCode0
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination ChangeCode0
Robust Stereo Feature Descriptor for Visual Odometry—0
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots—0
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion—0
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots—0
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion—0
PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust Navigation—0
Momo: Monocular Motion Estimation on Manifolds—0
Dense Piecewise Planar RGB-D SLAM for Indoor Environments—0
Monocular Visual Odometry for an Unmanned Sea-Surface Vehicle—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments—0
Topometric Localization with Deep Learning—0
Probabilistic RGB-D Odometry based on Points, Lines and Planes Under Depth UncertaintyCode0
Towards Visual Ego-motion Learning in Robots—0
PL-SLAM: a Stereo SLAM System through the Combination of Points and Line SegmentsCode0
Probabilistic Combination of Noisy Points and Planes for RGB-D Odometry—0
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect—0
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Benchmark Results

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