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 291–300 of 408 papers

TitleStatusHype
Learning by Inertia: Self-supervised Monocular Visual Odometry for Road Vehicles—0
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and DepthCode0
Continuous Direct Sparse Visual Odometry from RGB-D ImagesCode0
Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry—0
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based TasksCode0
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network—0
Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry—0
Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning—0
Sparse2Dense: From direct sparse odometry to dense 3D reconstruction—0
Pose Graph Optimization for Unsupervised Monocular Visual Odometry—0
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Benchmark Results

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