MTC-SLAM: Multi-Scale Tightly Coupled LiDAR-Inertial SLAM Method for Complex Environments With Dynamic Objects and Similar Features

Simultaneous Localization and Mapping (SLAM) is critical for autonomous driving. Compared with visual SLAM which suffers from variable lighting conditions and sparse features, Light Detection and Ranging (LiDAR) based solution is highly competitive since LiDAR can offer precise distance measurements and robust resistance to changes in illumination. However, current LiDAR SLAM techniques only estimate pose from a single scale, such that complex environments with dynamic objects and similar features can heavily affect the performance, leading to poor accuracy and robustness of pose estimation. To address these challenges, we present MTC-SLAM, a multi-scale and tightly coupled LiDAR-Inertial SLAM method. It begins with a coarse adjustment across six degrees of freedom at a larger scale, followed by adaptive step-size least squares updates for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{x}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{y}$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{yaw}$</tex-math></inline-formula> on a smaller scale, and dynamic interpolation updates for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{z}$</tex-math></inline-formula>, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{pitch}$</tex-math></inline-formula>, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{roll}$</tex-math></inline-formula>. The updated poses are integrated as odometer factors, alongside Global Positioning System (GPS) and loop closure factors, to construct a factor graph for backend optimization. Additionally, a multi-scale loop closure detection method is proposed to identify loops using multi-scale descriptors for global optimization. Extensive experiments on the MulRan dataset demonstrate that MTC-SLAM outperforms existing mainstream methods in both accuracy and robustness.