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 351–400 of 408 papers

TitleStatusHype
Dive Deeper into Rectifying Homography for Stereo Camera Online Self-Calibration—0
DLO: Direct LiDAR Odometry for 2.5D Outdoor Environment—0
Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles using Feedforward Neural Networks—0
Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments—0
DSVO: Direct Stereo Visual Odometry—0
Dynamic Dense RGB-D SLAM using Learning-based Visual Odometry—0
Dynamic Objects Segmentation for Visual Localization in Urban Environments—0
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching—0
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction—0
DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes—0
Efficient Domain Adaptation for Endoscopic Visual Odometry—0
Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
E-Graph: Minimal Solution for Rigid Rotation with Extensibility Graphs—0
EMR-MSF: Self-Supervised Recurrent Monocular Scene Flow Exploiting Ego-Motion Rigidity—0
Enhancing self-supervised monocular depth estimation with traditional visual odometry—0
Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences—0
eStonefish-scenes: A synthetically generated dataset for underwater event-based optical flow prediction tasks—0
Evaluation of the visual odometry methods for semi-dense real-time—0
Event-aided Direct Sparse Odometry—0
Event-based Stereo Visual Odometry with Native Temporal Resolution via Continuous-time Gaussian Process Regression—0
Event Camera-based Visual Odometry for Dynamic Motion Tracking of a Legged Robot Using Adaptive Time Surface—0
Evolution of Visual Odometry Techniques—0
Expansion of Visual Hints for Improved Generalization in Stereo Matching—0
Exploring Deep Spiking Neural Networks for Automated Driving Applications—0
Exploring Self-Attention for Visual Odometry—0
Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robots—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
FDMO: Feature Assisted Direct Monocular Odometry—0
Feature-based Event Stereo Visual Odometry—0
FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras—0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks—0
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation—0
Geometric-based Line Segment Tracking for HDR Stereo Sequences—0
Geometric Consistency for Self-Supervised End-to-End Visual Odometry—0
Geometry-Constrained Monocular Scale Estimation Using Semantic Segmentation for Dynamic Scenes—0
Global Pose Estimation with an Attention-based Recurrent Network—0
GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method—0
Gradient-based Camera Exposure Control for Outdoor Mobile Platforms—0
Ground Plane based Absolute Scale Estimation for Monocular Visual Odometry—0
GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion—0
Guided Feature Selection for Deep Visual Odometry—0
Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy—0
Improving Monocular Visual Odometry Using Learned Depth—0
Improving the Accuracy of Stereo Visual Odometry Using Visual Illumination Estimation—0
Improving Visual Feature Extraction in Glacial Environments—0
Incorporating GNSS Information with LIDAR-Inertial Odometry for Accurate Land-Vehicle Localization—0
Inertial Guided Uncertainty Estimation of Feature Correspondence in Visual-Inertial Odometry/SLAM—0
Information-Driven Direct RGB-D Odometry—0
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Benchmark Results

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