OpenVSLAM: A Versatile Visual SLAM Framework
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Updated
Feb 25, 2021
OpenVSLAM: A Versatile Visual SLAM Framework
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping
An Invitation to 3D Vision: A Tutorial for Everyone
[CoRL 21'] TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo
Robotics with GPU computing
Visual odometry package based on hardware-accelerated NVIDIA Elbrus library with world class quality and performance.
Unsupervised Scale-consistent Depth Learning from Video (IJCV2021 & NeurIPS 2019)
A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.
Depth and Flow for Visual Odometry
An Illumination-Robust Point-Line Visual Odometry (IROS 2023)
[ICRA'23] The official Implementation of "Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras"
A simple monocular visual odometry (part of vSLAM) by ORB keypoints with initialization, tracking, local map and bundle adjustment. (WARNING: Hi, I'm sorry that this project is tuned for course demo, not for real world applications !!!)
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
A bunch of state estimation algorithms
This repository is C++ OpenCV implementation of Stereo Odometry
Efficient monocular visual odometry for ground vehicles on ARM processors
Learning Depth from Monocular Videos using Direct Methods, CVPR 2018
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency (AAAI 2021)
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