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 211–220 of 408 papers

TitleStatusHype
Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories—0
Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure—0
Deep Monocular Visual Odometry for Ground Vehicle—0
A Review of Visual Odometry Methods and Its Applications for Autonomous Driving—0
Moving object detection for visual odometry in a dynamic environment based on occlusion accumulation—0
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
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Benchmark Results

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