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
Self-Improving Visual Odometry—0
Self-Supervised Deep Visual Odometry with Online Adaptation—0
Self-supervised Pretraining and Finetuning for Monocular Depth and Visual Odometry—0
Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry—0
Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry—0
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
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Benchmark Results

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