  Advancing maturity in mobile and legged robotics technologies is changing the
landscapes where robots are being deployed and found. This innovation calls for
a transformation in simultaneous localization and mapping (SLAM) systems to
support this new generation of service and consumer robots. No longer can
traditionally robust 2D lidar systems dominate while robots are being deployed
in multi-story indoor, outdoor unstructured, and urban domains with
increasingly inexpensive stereo and RGB-D cameras. Visual SLAM (VSLAM) systems
have been a topic of study for decades and a small number of openly available
implementations have stood out: ORB-SLAM3, OpenVSLAM and RTABMap.
  This paper presents a comparison of these 3 modern, feature rich, and
uniquely robust VSLAM techniques that have yet to be benchmarked against each
other, using several different datasets spanning multiple domains negotiated by
service robots. ORB-SLAM3 and OpenVSLAM each were not compared against at least
one of these datasets previously in literature and we provide insight through
this lens. This analysis is motivated to find general purpose, feature
complete, and multi-domain VSLAM options to support a broad class of robot
applications for integration into the new and improved ROS 2 Nav2 System as
suitable alternatives to traditional 2D lidar solutions.
