An Enhanced Multi-Sensor Simultaneous Localization and Mapping (SLAM) Framework with Coarse-to-Fine Loop Closure Detection Based on a Tightly Coupled Error State Iterative Kalman Filter

Author:

Yu Changhao1ORCID,Chao Zichen1,Xie Haoran2,Hua Yue1ORCID,Wu Weitao2

Affiliation:

1. Sino-French Engineer School, Nanjing University of Science and Technology, Nanjing 210094, China

2. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract

In order to attain precise and robust transformation estimation in simultaneous localization and mapping (SLAM) tasks, the integration of multiple sensors has demonstrated effectiveness and significant potential in robotics applications. Our work emerges as a rapid tightly coupled LIDAR-inertial-visual SLAM system, comprising three tightly coupled components: the LIO module, the VIO module, and the loop closure detection module. The LIO module directly constructs raw scanning point increments into a point cloud map for matching. The VIO component performs image alignment by aligning the observed points and the loop closure detection module imparts real-time cumulative error correction through factor graph optimization using the iSAM2 optimizer. The three components are integrated via an error state iterative Kalman filter (ESIKF). To alleviate computational efforts in loop closure detection, a coarse-to-fine point cloud matching approach is employed, leverging Quatro for deriving a priori state for keyframe point clouds and NanoGICP for detailed transformation computation. Experimental evaluations conducted on both open and private datasets substantiate the superior performance of the proposed method compared to similar approaches. The results indicate the adaptability of this method to various challenging situations.

Funder

National Key R&D Program of China

Key Laboratory of Thermal Management and Energy Utilization of Aircraft, Ministry of Industry and Information Technology

State Key Laboratory of Mechanics and Control for Aerospace Structures

Publisher

MDPI AG

Subject

Artificial Intelligence,Control and Optimization,Mechanical Engineering

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