RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation

TLDR

RTAB‑Map, an open‑source SLAM library released in 2013, evolved from an appearance‑based loop‑closure system to support diverse robots and sensors, yet selecting the most suitable SLAM approach remains challenging due to varying cost, accuracy, computation, and integration constraints, especially when comparing visual versus lidar methods. The authors extended RTAB‑Map to provide a single package that supports both visual and lidar SLAM, enabling users to implement and compare a wide range of 3D and 2D solutions across different robots and sensors. They introduced an extended RTAB‑Map version and applied it to a large set of real‑world datasets—including KITTI, EuRoC, TUM RGB‑D, and MIT Stata Center on a PR2 robot—to quantitatively and qualitatively compare visual and lidar SLAM configurations. The comparison revealed the strengths and limitations of visual and lidar SLAM setups from a practical perspective for autonomous navigation applications.

Abstract

Abstract Distributed as an open‐source library since 2013, real‐time appearance‐based mapping (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 constraints 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.