  Quadrupeds are robots that have been of interest in the past few years due to
their versatility in navigating across various terrain and utility in several
applications. For quadrupeds to navigate without a predefined map a priori,
they must rely on SLAM approaches to localize and build the map of the
environment. Despite the surge of interest and research development in SLAM and
quadrupeds, there still has yet to be an open-source package that capitalizes
on the onboard sensors of an affordable quadruped. This motivates the A1 SLAM
package, which is an open-source ROS package that provides the Unitree A1
quadruped with real-time, high performing SLAM capabilities using the default
sensors shipped with the robot. A1 SLAM solves the PoseSLAM problem using the
factor graph paradigm to optimize for the poses throughout the trajectory. A
major design feature of the algorithm is using a sliding window of fully
connected LiDAR odometry factors. A1 SLAM has been benchmarked against Google's
Cartographer and has showed superior performance especially with trajectories
experiencing aggressive motion.
