Here are some resources to get started with the implementation of SLAM:

* The book "Probabilistic Robotics" by Sevdalis and Arcas is a comprehensive resource that covers SLAM in detail. It provides a theoretical framework for robot localization and mapping, as well as practical algorithms for implementation.
* The SLAM Toolbox is a Python package that provides software for SLAM algorithms, including algorithms for monocular and stereo cameras, lidar sensors, and more. It includes examples and documentation to help you get started with SLAM.
* The ORB-SLAM2 library is a popular open-source SLAM library that provides both monocular and stereo camera SLAM implementations. It includes tutorials and examples to help you get started with SLAM.

Overall, SLAM is a complex topic that requires background in robotics, sensor fusion, and computer vision. It is recommended to have a solid understanding of these areas before diving into SLAM implementation.
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