This library provides a comprehensive framework for implementing and experimenting with SLAM systems. It leverages PyTorch's computational capabilities, automatic differentiation, and GPU acceleration to create a flexible, modular, and extensible SLAM solution.
- Interfaces for various sensors (RGB cameras, depth sensors, IMU, LiDAR)
- Sensor calibration utilities
- Data synchronization mechanisms
- Noise modeling and uncertainty handling
- Feature extraction and tracking
- Visual odometry pipeline
- Keyframe selection criteria
- Motion estimation algorithms
- Loop closure detection
- Factor graph optimization
- Bundle adjustment
- Pose graph optimization
- Map refinement
- Global consistency maintenance
- Point cloud generation and processing
- Volumetric mapping (TSDF, occupancy grids)
- Mesh reconstruction
- Semantic segmentation integration
- Dynamic object handling
- Kalman filtering (EKF, UKF)
- Particle filtering
- Probabilistic state representation
- Uncertainty propagation