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 231–240 of 408 papers

TitleStatusHype
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving—0
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents—0
Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas—0
Large Scale Photometric Bundle Adjustment—0
Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi—0
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching—0
Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods—0
What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector—0
WGANVO: Monocular Visual Odometry based on Generative Adversarial NetworksCode0
Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling—0
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Benchmark Results

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