Tightly-Coupled LiDAR-inertial Odometry for Wheel-based Skid Steering UGV

Li, Mengkai; Wang, Lei; Ren, Wenhu; Liu, Qi; Liu, Chaoyang

doi:10.1109/iciea54703.2022.10006021

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Wei et al [20] associated local ground planes in the form of Closest Point (CP) parameterization at different key-frames, and fuse the ground constraints into the graph factor optimization framework, experiments on multi-floor environments demonstrated the effectiveness of compressing the pose drift. The authors of [21] proposed a tightly coupled lidar and inertial SLAM framework based on factor graph optimization, and used a correlation method for local ground planes in the CP form at different key-frames. Kim et al [22] proposed a novel iterative closest point(ICP) algorithm for ground planes matching.…”

Section: Related Workmentioning

confidence: 99%

GCV-SLAM: Ground Constrained Visual SLAM Through Local Ground Planes

Fan,

Zhang,

Wang

et al. 2024

IEEE Access

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The classical visual Simultaneous Localization and Mapping(SLAM) algorithms assume that the camera moves in a free 3D space, while it is not valid for ground vehicles whose poses are influenced by ground surfaces. In recent years, researchers have focused on improving pose estimation accuracy by assuming planar motion which is more suitable to indoor environments. In this paper, we propose a lightweight stereo-visual SLAM framework for ground vehicles in road environments that tightly integrates constraints imposed by ground surfaces. We assume that the ground vehicle exhibits planar motion locally, and extract the observed local ground plane in each keyframe. To avoid the over-parameterization problem in the graph-factor-based optimization process, the Closest Point(CP) representation is adopted to describe the local ground plane. The roll, pitch, and position information provided by the local ground plane can be utilized to constrain the pose of the ground vehicle. Moreover, local ground planes can also be regarded as geometric features, enabling the construction of coplanarity constraints between ground map points and local ground planes, as well as between local ground planes in different keyframes. The KITTI odometry dataset was selected to validate the performance of our system, and the results demonstrated that our system could improve the accuracy and efficiency of ground vehicle localization in road environments.

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Section: Related Workmentioning

confidence: 99%

GCV-SLAM: Ground Constrained Visual SLAM Through Local Ground Planes

Fan,

Zhang,

Wang

et al. 2024

IEEE Access

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“…Hence, this study introduces a fusion structure that integrates LIO‐SAM with GPS information. While LIO‐SAM‐based approaches have included GPS information in their factor graph [6, 7], it is difficult to utilize covariance appropriately. In contrast, the proposed approach employs covariance intersection (CI) as a covariance‐based convergence technique to combine the two sets of information in the back‐end stage.…”

Section: Introductionmentioning

confidence: 99%

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

Kim,

Lee,

Lee

2024

Electronics Letters

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This letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting‐edge SLAM method, LIO‐SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature‐independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state‐of‐the‐art 3D SLAM technique. The convergence methods, namely “dynamic weight assignment” and “winner takes all”, are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. In the future, this research will focus on automating crop cultivation and harvesting by integrating the proposed system with robot arm control.

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Tightly-Coupled LiDAR-inertial Odometry for Wheel-based Skid Steering UGV

Cited by 2 publications

References 26 publications

GCV-SLAM: Ground Constrained Visual SLAM Through Local Ground Planes

GCV-SLAM: Ground Constrained Visual SLAM Through Local Ground Planes

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

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