  Distributed as an open source library since 2013, RTAB-Map started as an
appearance-based loop closure detection approach with memory management to deal
with large-scale and long-term online operation. It then grew to implement
Simultaneous Localization and Mapping (SLAM) on various robots and mobile
platforms. As each application brings its own set of contraints on sensors,
processing capabilities and locomotion, it raises the question of which SLAM
approach is the most appropriate to use in terms of cost, accuracy, computation
power and ease of integration. Since most of SLAM approaches are either visual
or lidar-based, comparison is difficult. Therefore, we decided to extend
RTAB-Map to support both visual and lidar SLAM, providing in one package a tool
allowing users to implement and compare a variety of 3D and 2D solutions for a
wide range of applications with different robots and sensors. This paper
presents this extended version of RTAB-Map and its use in comparing, both
quantitatively and qualitatively, a large selection of popular real-world
datasets (e.g., KITTI, EuRoC, TUM RGB-D, MIT Stata Center on PR2 robot),
outlining strengths and limitations of visual and lidar SLAM configurations
from a practical perspective for autonomous navigation applications.
