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 221–230 of 408 papers

TitleStatusHype
Semi-Dense Visual Odometry for RGB-D Cameras Using Approximate Nearest Neighbour Fields—0
Semi-Supervised Pipe Video Temporal Defect Interval Localization—0
Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints—0
SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks—0
SiLK: Simple Learned Keypoints—0
Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods—0
Single-Photon 3D Imaging with Equi-Depth Photon Histograms—0
SLAM in the Dark: Self-Supervised Learning of Pose, Depth and Loop-Closure from Thermal Images—0
Smooth Mesh Estimation from Depth Data using Non-Smooth Convex Optimization—0
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Benchmark Results

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