RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy

Liu, Lu; Mei, Tao; Niu, Runxin; Wang, Jie; Liu, Yongbo; Chu, Sen

doi:10.3390/app6060182

Cited by 25 publications

(15 citation statements)

References 33 publications

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“…By setting the region of interest, Yang et al used machine vision to accurately identify the crop lines between rows in the early growth stage of maize and extracted the navigation path of the plant protection robot in real time (Yang et al, 2022a). However, the inter-row environment in the middle and late stages of maize is a typical high-occlusion environment, with higher plant height and dense branches and leaves, seriously blocking light (Liu et al, 2016;Xie et al, 2019). When the ambient light intensity is weak, information loss will occur when using machine vision to obtain inter-row navigation information (Chen et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

Xie

Liu

et al. 2022

Front. Plant Sci.

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In the middle and late stages of maize, light is limited and non-maize obstacles exist. When a plant protection robot uses the traditional visual navigation method to obtain navigation information, some information will be missing. Therefore, this paper proposed a method using LiDAR (laser imaging, detection and ranging) point cloud data to supplement machine vision data for recognizing inter-row information in the middle and late stages of maize. Firstly, we improved the YOLOv5 (You Only Look Once, version 5) algorithm based on the characteristics of the actual maize inter-row environment in the middle and late stages by introducing MobileNetv2 and ECANet. Compared with that of YOLOv5, the frame rate of the improved YOLOv5 (Im-YOLOv5) increased by 17.91% and the weight size decreased by 55.56% when the average accuracy was reduced by only 0.35%, improving the detection performance and shortening the time of model reasoning. Secondly, we identified obstacles (such as stones and clods) between the rows using the LiDAR point cloud data to obtain auxiliary navigation information. Thirdly, the auxiliary navigation information was used to supplement the visual information, so that not only the recognition accuracy of the inter-row navigation information in the middle and late stages of maize was improved but also the basis of the stable and efficient operation of the inter-row plant protection robot was provided for these stages. The experimental results from a data acquisition robot equipped with a camera and a LiDAR sensor are presented to show the efficacy and remarkable performance of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

Xie

Liu

et al. 2022

Front. Plant Sci.

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“…Localization and navigation have attracted much attention in recent years with respect to a wide range of applications, particularly for self-driving cars, service robots, and unmanned aerial vehicles, etc. Several types of sensors are utilized for localization and navigation, such as global navigation satellite systems (GNSSs) [ 1 ], laser lidar [ 2 , 3 ], inertial measurement units (IMUs), and cameras [ 4 , 5 ]. However, they have obvious respective drawbacks: GNSSs only provide reliable localization information if there is a clear sky view [ 6 ]; laser lidar suffers from a reflection problem for objects with glass surfaces [ 7 ]; measurements from civilian IMUs are noisy, such that inertial navigation systems may drift quickly due to error accumulation [ 8 ]; and monocular simultaneous localization and mapping (SLAM) can only recover the motion trajectory up to a certain scale and it tends to be lost when the camera moves fast or illumination changes dramatically [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features

Zhao²,

Guo

et al. 2018

Sensors

221

140

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To address the problem of estimating camera trajectory and to build a structural three-dimensional (3D) map based on inertial measurements and visual observations, this paper proposes point–line visual–inertial odometry (PL-VIO), a tightly-coupled monocular visual–inertial odometry system exploiting both point and line features. Compared with point features, lines provide significantly more geometrical structure information on the environment. To obtain both computation simplicity and representational compactness of a 3D spatial line, Plücker coordinates and orthonormal representation for the line are employed. To tightly and efficiently fuse the information from inertial measurement units (IMUs) and visual sensors, we optimize the states by minimizing a cost function which combines the pre-integrated IMU error term together with the point and line re-projection error terms in a sliding window optimization framework. The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only.

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“…To date, many technologies, such as sensors, RTK-GPS (RTK: Real-time kinematic; GPS: Global Positioning System), machine vision, LiDAR, and ultrasonic geomagnetic position, have been developed to study the autonomous navigation of robots [11,12]. However, machine vision is affected by the working environment and lighting conditions to a large extent, and technologies involved in it, such as image processing, image analysis, camera calibration, and the extraction of navigation parameters, make it rather difficult to apply in agriculture [13,14,15,16]. The application of GPS is affected by interruptions in the satellite signal [11,17].…”

Section: Introductionmentioning

confidence: 99%

A Rubber-Tapping Robot Forest Navigation and Information Collection System Based on 2D LiDAR and a Gyroscope

Zhang

Yong

Chen

et al. 2019

Sensors

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Natural rubber is widely used in human life because of its excellent quality. At present, manual tapping is still the main way to obtain natural rubber. There is a sore need for intelligent tapping devices in the tapping industry, and the autonomous navigation technique is of great importance to make rubber-tapping devices intelligent. To realize the autonomous navigation of the intelligent rubber-tapping platform and to collect information on a rubber forest, the sparse point cloud data of tree trunks are extracted by the low-cost LiDAR and a gyroscope through the clustering method. The point cloud is fitted into circles by the Gauss–Newton method to obtain the center point of each tree. Then, these center points are threaded through the Least Squares method to obtain the straight line, which is regarded as the navigation path of the robot in this forest. Moreover, the Extended Kalman Filter (EKF) algorithm is adopted to obtain the robot’s position. In a forest with different row spacings and plant spacings, the heading error and lateral error of this robot are analyzed and a Fuzzy Controller is applied for the following activities: walking along one row with a fixed lateral distance, stopping at fixed points, turning from one row into another, and collecting information on plant spacing, row spacing, and trees’ diameters. Then, according to the collected information, each tree’s position is calculated, and the geometric feature map is constructed. In a forest with different row spacings and plant spacings, three repeated tests have been carried out at an initial speed of 0.3 m/s. The results show that the Root Mean Square (RMS) lateral errors are less than 10.32 cm, which shows that the proposed navigation method provides great path tracking. The fixed-point stopping range of the robot can meet the requirements for automatic rubber tapping of the mechanical arm, and the average stopping error is 12.08 cm. In the geometric feature map constructed by collecting information, the RMS radius errors are less than 0.66 cm, and the RMS plant spacing errors are less than 11.31 cm. These results show that the method for collecting information and constructing a map recursively in the process of navigation proposed in the paper provides a solution for forest information collection. The method provides a low-cost, real-time, and stable solution for forest navigation of automatic rubber tapping equipment, and the collected information not only assists the automatic tapping equipment to plan the tapping path, but also provides a basis for the informationization and precise management of a rubber plantation.

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RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy

Cited by 25 publications

References 33 publications

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

Inter-row information recognition of maize in the middle and late stages via LiDAR supplementary vision

PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features

A Rubber-Tapping Robot Forest Navigation and Information Collection System Based on 2D LiDAR and a Gyroscope

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