Visual Odometry
95 papers with code • 0 benchmarks • 21 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
Benchmarks
These leaderboards are used to track progress in 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
Most implemented papers
The Double Sphere Camera Model
We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i. e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians.
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities.
DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
ChiWeiHsiao/DeepVO-pytorch
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This paper presents a novel end-to-end framework for monocular VO by using deep Recurrent Convolutional Neural Networks (RCNNs).
The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
For trajectory evaluation, we also provide accurate pose ground truth from a motion capture system at high frequency (120 Hz) at the start and end of the sequences which we accurately aligned with the camera and IMU measurements.
A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors
We validate the performance of our system on public datasets and through real-world experiments with multiple sensors.
Towards Better Generalization: Joint Depth-Pose Learning without PoseNet
B1ueber2y/TrianFlow
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CVPR 2020
In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning.
PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments
This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image.
Stereo relative pose from line and point feature triplets
In this work, we present two minimal solvers for the stereo relative pose.
Direct Sparse Odometry
We propose a novel direct sparse visual odometry formulation.
Reducing Drift in Visual Odometry by Inferring Sun Direction Using a Bayesian Convolutional Neural Network
We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, where the sun is typically not visible.
