This repository contains everything you need to start working with Lidar -based SLAM in Python. (There is also support for Matlab, C++, and Java; however, because of the popularity of Python for this kind of work, I am no longer updating the code for those languages.) BreezySLAM works with Python 3 on Linux and Mac OS X, and with C++ on Linux and Windows. By using Python C extensions, we were able to get the Python and Matlab versions to run as fast as C++. For maximum efficiency on 32-bit platforms, we use Streaming SIMD extensions (Intel) and NEON (ARMv7) in the compute-intensive part of the code. BreezySLAM was inspired by the Breezy approach to Graphical User Interfaces developed by my colleague Ken Lambert: an object-oriented Application Programming Interface that is simple enough for beginners to use, but that is efficient enough to scale-up to real world problems; for example, the mapping of an entire floor of a house, shown in the image above-right, made by a BreezySLAM user.