pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.

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

Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM

[TRO 2025] AirVO upgrades to AirSLAM

Robotics with GPU computing

[CoRL 21'] TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo

[ICRA 2025 Best Paper] MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry

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.

Unsupervised Scale-consistent Depth Learning from Video (IJCV2021 & NeurIPS 2019)

Depth and Flow for Visual Odometry

Educational/research monocular visual odometry implementation with ORB features, initialization, tracking, local mapping, and bundle adjustment in C++.

[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"

Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction

ROS 2 wrapper for the ZED SDK

EndoSLAM Dataset and an Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner

A bunch of state estimation algorithms