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.

MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects

Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects

[CVPR 2023] vMAP: Vectorised Object Mapping for Neural Field SLAM

[ICCV-2025] TurboReg: TurboClique for Robust and Efficient Point Cloud Registration

KinectFusion implemented in Python with PyTorch

MD-SLAM: Multi-cue Direct SLAM. Implements the first photometric LiDAR SLAM pipeline, that works withouth any explicit geometrical assumption. Universal approach, working independently for RGB-D and LiDAR.

Code for CVPR 2018 paper --- Texture Mapping for 3D Reconstruction with RGB-D Sensor

用python学习rgbd-slam系列

Photometric SLAM and Bundle Adjustment for RGB-D and LiDAR in CUDA.

[ECCV'18 Oral] PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction

Lightweight Dense Visual Odometry - a header-only C++ library for real-time dense RGB-D odometry.

The package is common Slam combined with DeepLab-V2-CRF Library.

Code for ICRA 2019 work "MID-Fusion Octree-based Object-Level Multi-Instance Dynamic SLAM"

Code for RA-L work "Deep Probabilistic Feature-metric Tracking"

Visual SLAM learning and training

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Dockerfile for the use of [ElasticFusion](https://github.com/mp3guy/ElasticFusion)

Implemented a RGBD vSLAM in C++

ROS raspberry pi "roomba" like robot

Code for learning SLAMBOOK

Kinect support for ORB-SLAM2

Building a RGBD ptam meant to be used for industrial and educational purposes

Autonomous Driving Turtlebot using BiSeNet segmentation network on RGBD data

Modified version of RGBDSLAMv2 which from http://github.com/felixendres/rgbdslam_v2/archive/kinetic.zip

Visual-Inertial SLAM performed with an RGBD and IMU dataset.

RGB-D Semantic Sampling developed through OpenCV in Python based on images take by IntelSense RGB-D camera

Project 4 - Udacity Robotics Software Engineer Nanodegree Program

Ready Docker images for SOTA RGBD SLAM methods