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.

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

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

Depth and Flow for Visual Odometry

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

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

Learning Depth from Monocular Videos using Direct Methods, CVPR 2018

Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency (AAAI 2021)

[ECCV 2024 Oral] COMO: Compact Mapping and Odometry

Implementation of ICRA 2019 paper: Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation

Deep Learning for Visual-Inertial Odometry

Simultaneous Visual Odometry, Object Detection, and Instance Segmentation

Implementation of the paper "Transformer-based model for monocular visual odometry: a video understanding approach".

Implementation of Open-World Visual Odometry with Temporal Dynamics Awareness (CVPR'26)

[ICCV 2021] Official implementation of "The Surprising Effectiveness of Visual Odometry Techniques for Embodied PointGoal Navigation"

Code for T-ITS paper "Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry" and for ICRA paper "Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks".

[ECCV 2022]JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes

Implementing different steps to estimate the 3D motion of the camera. Provides as output a plot of the trajectory of the camera.

Visual odometry using optical flow and neural networks

Implementation of DeepVO (ICRA 2017)