SLAM Toolbox: SLAM for the dynamic world

Developments in the field of mobile robotics and autonomous driving have resulted in the use of robots and vehicles in retail stores, hospitals, warehouses, on the roads, and on sidewalks. These deployed areas are both dynamic and frequently massive in scale. The average size of a Walmart store is over 16,000m2 and a single square block in Chicago is over 21,000m2. Retail and warehouse spaces can change drastically through out the year and the state of roadways can be changing by the hour. Much work has been made to address changing environments in robot perception (Macenski et al.), but less has been built in open-source to represent maps of dynamic spaces. For fully autonomous deployed systems to operate in these large and changing environments, they require tools that can be used to accurately map an area specified for their operation,update it over time, and scale to handle mapping of some of the largest indoor and outdoor spaces imaginable. The field of Simultaneous Localization and Mapping (SLAM) aims to solve this problem using a variety of sensor modalities, including: laser scanners, radars, cameras,encoders, gps and IMUs. The most commonly used perception sensor used for localization and mapping in industrial environments is the laser scanner. SLAM methods using laser scanners are generally considered the most robust in the SLAM field and can provide accurate positioning in the presence of dynamic obstacles and changing environments(Cole & Newman,2006). Previously existing open-source laser scanner SLAM algorithms available to users in the popular Robot Operating System (ROS) include GMapping, Karto, Cartographer, and Hector. However, few of these can build accurate maps of large spaces on the scale of the aver-age Walmart store. Even fewer can do so in real-time using the mobile processor typically found in mobile robot systems today. The only package that could accomplish the above was Cartographer, however it was abandoned by Google and it is no longer maintained. We propose a new fully open-source ROS package, SLAM Toolbox, to solve this problem.SLAM Toolbox builds on the legacy of Open Karto (Konolige et al.,2010), the open-source library from SRI International, providing not only accurate mapping algorithms, but a variety of other tools and improvements. SLAM Toolbox provides multiple modes of mapping de-pending on need, asynchronous and asynchronous, utilities such as kinematic map merging, a localization mode, multi-session mapping, improved graph optimization, substantially reduced compute time, and prototype lifelong and distributed mapping applications.This package, slam_toolbox is open-source under an LGPLv2.1 athttps://github.com/SteveMacenski/slam_toolbox.gitand is available in every current ROS distribution. It was also selected as the new default SLAM vendor in ROS 2, the second generation of robot operating systems, replacing GMapping. SLAM Toolbox was integrated into the new ROS 2Navigation2 project, providing real-time positioning in dynamic environments for autonomous navigation (Macenski et al.). It has been shown to map spaces as large as 24,000m2, or 250,000ft2, in real-time by non-expert technicians