Mercator Descriptor: A Novel Global Descriptor for Loop Closure Detection in LiDAR SLAM

Loop closure detection is a fundamental technique in simultaneous localization and mapping (SLAM), playing a crucial role in the map-building process. Inspired by the Mercator projection and depth maps, this article presents a novel loop closure detection method for LiDAR SLAM, termed the Mercator descriptor. Our approach applies the Mercator projection principles to the point cloud data captured by the sensor in each frame and extracts depth information to generate descriptors. Additionally, we utilize a two-step identification strategy, from coarse to fine, to improve the accuracy and recall of loop closure detection. Testing on public datasets demonstrates that our algorithm achieves an average maximum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> score of 94.57%, performing well across various datasets. Notably, we have integrated our algorithm into existing SLAM frameworks, and results from multiple datasets show that our algorithm can perform real-time and efficient map optimization. To share our findings and contribute to the community, we have open-sourced our code on GitHub: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wangzika/Mercator-Descriptor.git</uri>.