A Forest 3-D Lidar SLAM System for Rubber-Tapping Robot Based on Trunk Center Atlas

Nie, Fuyu; Zhang, Weimin; Wang, Yang; Shi, Yongliang

doi:10.1109/tmech.2021.3120407

Cited by 10 publications

(14 citation statements)

References 33 publications

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“…Haselich's split-merge clustering algorithm was used to avoid wrongly merging clusters when they are close enough in distance. Nie, F. et al [36] used density-based spatial clustering of applications with noise (DBSCAN) with density field correction to segment the LIDAR point cloud, aiming to reduce the effect of distance on the density of LIDAR points. Leveraging the formidable parallel computation prowess inherent in GPUs, Zhao et al [37,38] introduced an innovative online seed optimization strategy aimed at accelerating the mean-shift algorithm.…”

Section: Trunk Detectionmentioning

confidence: 99%

Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

et al. 2023

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To address the practical navigation issues of rubber-tapping robots, this paper proposes an active navigation system guided by trunk detection for a rubber-tapping robot. A tightly coupled sliding-window-based factor graph method is proposed for pose tracking, which introduces normal distribution transform (NDT) measurement factors, inertial measurement unit (IMU) pre-integration factors, and prior factors generated by sliding window marginalization. To actively pursue goals in navigation, a distance-adaptive Euclidean clustering method is utilized in conjunction with cylinder fitting and composite criteria screening to identify tree trunks. Additionally, a hybrid map navigation approach involving 3D point cloud map localization and 2D grid map planning is proposed to apply these methods to the robot. Experiments show that our pose-tracking approach obtains generally better performance in accuracy and robustness compared to existing methods. The precision of our trunk detection method is 93% and the recall is 87%. A practical validation is completed in robot rubber-tapping tasks of a real rubber plantation. The proposed method can guide the rubber-tapping robot in complex forest environments and improve efficiency.

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Section: Trunk Detectionmentioning

confidence: 99%

Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

et al. 2023

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“…In recent research work [34,47], state-of-the-art SLAM methods are often used as a proxy for ground truth during evaluation. In [48], Cartographer [49] shows a superior performance over other SLAM systems in indoor scenes, and we adopt this as ground truth.…”

Section: Localization Evaluationmentioning

confidence: 99%

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

Yin¹,

Lin²,

Yeoh³

2022

Preprint

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Conventional sensor-based localization relies on high-precision maps. These maps are generally built using specialized mapping techniques, which involve high labor and computational costs. While in the architectural, engineering and construction industry, building information models (BIMs) are available and can provide informative descriptions of environments. This paper explores an effective way to localize a mobile 3D LiDAR sensor in BIM considering both geometric and semantic properties. Specifically, we first convert original BIM to semantic maps using categories and locations of BIM elements. After that, a coarse-to-fine semantic localization is performed to align laser points to the map via iterative closest point registration. The experimental results show that the semantic localization can track the pose with only scan matching and present centimeter-level errors over 340 meters traveling, thus demonstrating the feasibility of the proposed mapping-free localization framework. The results also show that using semantic information can help reduce localization errors in BIM.

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“…Meanwhile, the noise can be caused and may have harm on the operator. In recent years, many scholars have attempted to use intelligent rubber tapping machinery, including rubber tapping robots [8][9][10][11][12][13] and fixed rubber tapping machines [14][15][16][17] instead of manual rubber tapping. They want to realize the precise tapping depth control of intelligent rubber tapping machinery by introducing industrial ranging technology involving machine vision [18][19][20], laser ranging [21,22] and ultrasonic ranging [9].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Planetary Rotor with Variable Speed Self Rotation and Uniform Eccentric Revolution in the Rubber Tapping Machinery

Cao,

Fan,

Xiao

et al. 2024

Forests

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Natural rubber is a critical material that is essential to industry and transportation. In order to reduce the cost of rubber tapping and improve the efficiency and profitability of rubber production, the 4GXJ-2 portable electric rubber cutter and automatic rubber tapping robot have been developed. In their vibration tool holder, the planetary rotor with variable speed self rotation and uniform eccentric revolution is the most important transmission component, and its instability will cause irregular vibration of the tapping tool, thereby reducing the accuracy of vibration cutting and increasing noise. Base on the ANCF (Absolute Nodal Coordinate Formulation) 3D-beam element and 3D REF (3D Ring on Elastic Foundation), a novel eccentric 3D REF model of a planetary rotor is proposed. By introducing multiple coordinate systems, the coupled motion of uniform eccentric revolution, variable speed self rotation and flexible deformation is decomposed and the influences of these motions on the centrifugal force and Coriolis force are more clearly derived. The model is degraded and validated by comparing with other examples of a rotating circular ring model and uniformly eccentrically revolving annular plate. According to the Floquet theory and Runge−Kutta method, the unstable region of revolution speed of a planetary rotor in rubber tapping machinery is predicted as [817 rad/s, 909 rad/s], [1017 rad/s, 1095 rad/s] and [1263 rad/s,1312 rad/s]. Compared with the rubber-tapping experiment of rubber tapping machinery, the validity of the proposed model is further verified. This model provides important design references for the speed settings of those rubber tapping machines.

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A Forest 3-D Lidar SLAM System for Rubber-Tapping Robot Based on Trunk Center Atlas

Cited by 10 publications

References 33 publications

Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

Active Navigation System for a Rubber-Tapping Robot Based on Trunk Detection

Semantic localization on BIM-generated maps using a 3D LiDAR sensor

Stability Analysis of Planetary Rotor with Variable Speed Self Rotation and Uniform Eccentric Revolution in the Rubber Tapping Machinery

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