luigifreda/pyslam
Pyslam
pyslam is a framework for Simultaneous Localization and Mapping that combines Python flexibility with C++ performance. It is a sparse SLAM implementation designed to map environment geometry and track device location by processing image frames into 3D points.
The project features a bridge for exposing high-performance C++ classes to Python scripts using zero-copy memory sharing. This integration allows for switching between a scripting interface for rapid prototyping and a compiled core for execution speed.
The system includes a spatial map optimizer to refine 3D point and camera pose estimates using mathematical optimization and nearest neighbor search. It also provides utilities for spatial data serialization to convert mapping and tracking states into standardized formats for persistence and cross-language compatibility.
Features
- Simultaneous Localization and Mapping - Provides a complete framework for tracking camera movement and mapping 3D environments from image frames.
- Visual SLAM Implementations - Implements a full visual SLAM system that tracks camera movement and maps 3D environments in real time.
- Sparse Visual Mapping - Implements a sparse visual map by tracking a subset of distinct 3D landmarks instead of dense voxel grids.
- Pose Optimization - Employs numerical optimization methods to refine camera trajectories and landmark positions by minimizing reprojection error.
- Point Cloud Optimization - Refines camera poses and environment geometry using mathematical optimization and nearest neighbor searches.
- KD-Trees - Uses k-d trees to efficiently organize 3D points and accelerate nearest-neighbor searches for spatial matching.
- Local Mapping Optimizations - Utilizes parallel threading to maintain real-time performance by optimizing the local map in the camera's immediate vicinity.
- SLAM Frameworks - Provides a comprehensive framework for SLAM that combines Python's flexibility with C++'s computational performance.
- Visual SLAM Optimizations - Refines 3D point and camera pose estimates using mathematical optimization to improve mapping accuracy.
- Python-C Interfaces - Provides a performance-oriented bridge connecting high-level Python scripting with optimized C++ binaries.
- C++ Language Bridges - Implements a C++ language bridge that maps high-performance classes and types to a flexible Python scripting interface.
- Python-C++ Bindings - Exposes high-performance C++ classes to Python via pybind11 for combined scripting flexibility and execution speed.
- Shared Memory Transports - Uses zero-copy memory sharing to move large spatial data structures between language runtimes without duplicating memory.
- Cross-Language Data Exchange - Enables bidirectional movement of spatial data between Python and C++ using high-performance zero-copy arrays.
- Implementation Language Switching - Implements a unified interface to switch between Python prototypes and a compiled C++ core for balanced development speed and execution performance.
- Simultaneous Localization and Mapping - Monocular visual odometry pipeline in Python.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
