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GNSS/LiDAR/IMU Fusion Odometry Based on Tightly-Coupled Nonlinear Observer in Orchard

文献类型： 外文期刊

作者： Sun, Na 1 ; Qiu, Quan 3 ; Li, Tao 2 ; Ru, Mengfei 2 ; Ji, Chao 5 ; Feng, Qingchun 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

3.Beijing Univ Agr, Coll Intelligent Sci & Engn, Beijing 102206, Peoples R China

4.Shanxi Agr Univ, Coll Agr Engn, Jinzhong 030801, Peoples R China

5.Xinjiang Acad Agr & Reclamat Sci, Shihezi 832000, Peoples R China

关键词： robot localization; state estimation; information fusion; nonlinear observer; agricultural environment

期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）

ISSN：

年卷期： 2024 年 16 卷 16 期

页码：

收录情况： SCI

摘要： High-repetitive features in unstructured environments and frequent signal loss of the Global Navigation Satellite System (GNSS) severely limits the development of autonomous robot localization in orchard settings. To address this issue, we propose a LiDAR-based odometry pipeline GLIO, inspired by KISS-ICP and DLIO. GLIO is based on a nonlinear observer with strong global convergence, effectively fusing sensor data from GNSS, IMU, and LiDAR. This approach allows for many potentially interfering and inaccessible relative and absolute measurements, ensuring accurate and robust 6-degree-of-freedom motion estimation in orchard environments. In this framework, GNSS measurements are treated as absolute observation constraints. These measurements are tightly coupled in the prior optimization and scan-to-map stage. During the scan-to-map stage, a novel point-to-point ICP registration with no parameter adjustment is introduced to enhance the point cloud alignment accuracy and improve the robustness of the nonlinear observer. Furthermore, a GNSS health check mechanism, based on the robot's moving distance, is employed to filter reliable GNSS measurements to prevent odometry crashed by sensor failure. Extensive experiments using multiple public benchmarks and self-collected datasets demonstrate that our approach is comparable to state-of-the-art algorithms and exhibits superior localization capabilities in unstructured environments, achieving an absolute translation error of 0.068 m and an absolute rotation error of 0.856 degrees.

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