Hyla-SLAM: Toward Maximally Scalable 3D LiDAR-Based SLAM Using Dynamic Memory Management and Behavior Trees

Abstract

Solving the Simultaneous Localization and Mapping (SLAM) problem is essential for most mobile robotics applications that do not have an a priori environment representation. The SLAM problem is well-studied, with previous works demonstrating impressive results with a variety of robots, sensors, and environments. However, despite the widespread need for SLAM solutions across a broad spectrum of robotics disciplines and applications, it remains challenging to quickly and easily scale existing solutions to new robots, environments, and tasks. In this paper, we address this problem by introducing Hyla-SLAM, a framework for 3D LiDAR-based SLAM that uses dynamic memory management to efficiently create and manage maps of environments of arbitrary size and density. Hyla-SLAM also scales to diverse systems and applications by using behavior trees to maximize runtime flexibility and extensibility. We demonstrate the scalability of Hyla-SLAM in experiments using datasets collected in North America, Europe, and Asia to generate a single, unified global-scale map thousands of kilometers across that can be efficiently accessed and expanded. We also show experiments using the behavior tree interface to make robot- or task-informed modifications that enable deployment on heterogeneous robots with varying system constraints. These results demonstrate the framework’s ability to efficiently create and manage huge maps while generalizing to a wide range of systems and applications with minimal reconfiguration. We release Hyla-SLAM’s code implementation<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> open-source.