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 221–230 of 408 papers

TitleStatusHype
Depth Completion using Piecewise Planar Model—0
Exploring Self-Attention for Visual Odometry—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Unsupervised Deep Persistent Monocular Visual Odometry and Depth Estimation in Extreme Environments—0
Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories—0
On Deep Learning Techniques to Boost Monocular Depth Estimation for Autonomous Navigation—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
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Benchmark Results

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