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 301–310 of 408 papers

TitleStatusHype
A Unified Formulation for Visual Odometry—0
Sparse Representations for Object and Ego-motion Estimation in Dynamic ScenesCode0
Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry—0
ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera SystemsCode0
Beyond Photometric Loss for Self-Supervised Ego-Motion EstimationCode0
A Generative Map for Image-based Camera LocalizationCode0
Exploring Deep Spiking Neural Networks for Automated Driving Applications—0
A General Optimization-based Framework for Local Odometry Estimation with Multiple SensorsCode0
Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences—0
SfMLearner++: Learning Monocular Depth & Ego-Motion using Meaningful Geometric Constraints—0
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Benchmark Results

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