Visual Odometry
98 papers with code • 1 benchmarks • 22 datasets
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
Libraries
Use these libraries to find Visual Odometry models and implementationsDatasets
KITTI
TUM RGB-D
Virtual KITTI
TartanAir
Event-Camera Dataset
Virtual KITTI 2
4Seasons
TUM monoVO
EndoSLAM
Latest papers
Converting Depth Images and Point Clouds for Feature-based Pose Estimation
Compared to Bearing Angle images, our method yields brighter, higher-contrast images with more visible contours and more details.
Transformer-based model for monocular visual odometry: a video understanding approach
In this work, we deal with the monocular visual odometry as a video understanding task to estimate the 6-DoF camera's pose.
Modality-invariant Visual Odometry for Embodied Vision
memmelma/vo-transformer
•
•
CVPR 2023
Our model outperforms previous methods while training on only a fraction of the data.
SiLK -- Simple Learned Keypoints
Keypoint detection & descriptors are foundational tech-nologies for computer vision tasks like image matching, 3D reconstruction and visual odometry.
FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets
The stereo datasets include synchronized stereo images in dynamic underwater environments with objects of known-size.
Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry
Monocular visual odometry consists of the estimation of the position of an agent through images of a single camera, and it is applied in autonomous vehicles, medical robots, and augmented reality.
Orbeez-SLAM: A Real-time Monocular Visual SLAM with ORB Features and NeRF-realized Mapping
A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated.
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
SF2SE3 then iteratively (1) samples pixel sets to compute SE(3)-motion proposals, and (2) selects the best SE(3)-motion proposal with respect to a maximum coverage formulation.
DytanVO: Joint Refinement of Visual Odometry and Motion Segmentation in Dynamic Environments
geniussh/dytanvo
•
•
Learning-based visual odometry (VO) algorithms achieve remarkable performance on common static scenes, benefiting from high-capacity models and massive annotated data, but tend to fail in dynamic, populated environments.
Deep Patch Visual Odometry
princeton-vl/dpvo
•
•
NeurIPS 2023
DPVO disproves this assumption, showing that it is possible to get the best accuracy and efficiency by exploiting the advantages of sparse patch-based matching over dense flow.
