Stereo SLAM System Development
Robust 1D and 2D SLAM pipeline utilizing C++, OpenCV, and Pose-Graph Optimization.
Building a reliable Simultaneous Localization and Mapping (SLAM) system is one of the foundational challenges in modern robotics. For this project, I engineered a robust 1D and 2D SLAM pipeline from scratch, leveraging the power of C++ and OpenCV.
Core Technical Implementation
The system architecture focuses on high-accuracy trajectory estimation and environmental reconstruction:
- Mathematical Foundations: Implemented Stereo Reconstruction and Perspective-n-Point (PnP) algebraic solvers to translate 2D camera feeds into mathematically rigorous 3D spatial points.
- Bundle Adjustment: Integrated local and global Bundle Adjustment techniques to jointly refine the 3D coordinates describing the scene geometry and the parameters of the relative motion, significantly reducing reprojection errors.
- Pose-Graph Optimization: A major hurdle in SLAM is accumulated drift over long operational distances. I applied advanced Pose-Graph Optimization algorithms to detect loop closures and correct trajectory drift. This drastically enhanced the global localization accuracy of the mapping process.
By bridging theoretical mathematical models with highly optimized C++ code, this project successfully demonstrates a low-latency, highly accurate tracking and mapping solution suitable for real-time robotic navigation.