Recent developments and applications of simultaneous localization and mapping in agriculture

Ding, Haizhou; Zhang, Baohua; Zhou, Jun; Yan, Yaxuan; Tian, Guohang; Gu, Bin

doi:10.1002/rob.22077

Cited by 43 publications

(25 citation statements)

References 216 publications

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“…The vertical movement smoothness indicator is defined in [36] to express the degree of smoothness of the pose movement in the vertical direction. The vertical movement smoothness indicator d z is given by equation (1).…”

Section: Avision Processing Front Endmentioning

confidence: 99%

“…Evaluation metrics: this work uses the mean of absolute trajectory error (ATE, as defined in Sturm et al [45]) in (8), and the ratio of ATE over the length of the trajectory [22] as evaluation metrics.…”

Section: A Dataset and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…With the rapid development of industries such as unmanned delivery vehicles and autonomous robots, the application of autonomous mobile robots in agricultural automation is also gaining attention [1]. There are many potential application scenarios for robots in agricultural production, such as using robots to monitor crops, pick fruit, spray agrochemicals, and weed control [1], [2]. The ability to accurately locate an automated agricultural machine is a critical factor in determining the feasibility of automated agriculture [3], [4], so how robots can be precisely localized in agricultural environments is a current research hotspot.…”

Section: Introductionmentioning

confidence: 99%

“…Robot localization is the process of determining where a robot is located regarding its environment. Robot localization technology employed in agricultural robots uses various sensors, such as on-board Global Navigation Satellite System (GNSS) [5], Light Detection and Ranging (LiDAR) scanners [6], cameras [7], and Inertial Measurement Units (IMU) [8], to localize the robot relative to the crop lines or tree row in order to automatic steering. Traditional localization methods use high-end real-time kinematic GNSS devices like GPS-RTK [9] to provide accurate location information.…”

Section: Introductionmentioning

confidence: 99%

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Monocular Visual-Inertial Odometry for Agricultural Environments

et al. 2022

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The accuracy of autonomous robot localization using a monocular visual-inertial odometry system (VIO) is significantly reduced in an agricultural environment compared to an urban and indoor environment due to the unstructured scenes with unstable features, variation of light conditions, and rugged terrain. To address those challenges, we propose a monocular VIO system with modifications to the existing state-of-the-art monocular VIO system VINS-mono. Three modules of VINS-mono have improved in this work: vision processing front end, pose optimization, failure detection and recovery. In the vision processing front-end module, we proposed a keyframe selection algorithm base on vertical movement smoothness verification to prevent the rapid loss of feature tracks. In the pose optimization module, some feature points are removed using a depth-limited approach to improve the efficiency and accuracy of trajectory tracking. In the failure detection and recovery module, we propose a failure recovery method using the old trajectory poses, which can recover the trajectory tracking process interrupted by the failure and alleviate the orientation drift. Experiments on the Rosario dataset have shown that our system outperforms VINS-mono, and the absolute trajectory error in the agricultural environment can be at least reduced by 69% compared with the VINS-mono, which can effectively improve the localization accuracy of agriculture robots in the agricultural environment.

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Section: Avision Processing Front Endmentioning

confidence: 99%

Section: A Dataset and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monocular Visual-Inertial Odometry for Agricultural Environments

et al. 2022

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LiDAR‐based object detection for agricultural robots

Stronzek‐Pfeifer,

Chemmadan,

Patel

et al. 2023

Proc Appl Math and Mech

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Light detection and ranging (LiDAR) sensors have proven to be a valuable tool to gather spatial information about the environment and are a crucial component in perception of autonomous systems. In the agricultural domain, state‐of‐the‐art algorithms for detection, classification, and tracking often utilize a combination of LiDAR and camera by fusing both semantic and spatial information. This is in part due to the availability of fast algorithms specifically optimized to make use of the convenient 2D data structure of RGB images. Still, there are limitations relying on cameras in agriculture because they highly depend on favorable lighting conditions and generally lack explicit geometric information, whereas LiDAR is especially advantageous in that regard as well as regarding range. This makes LiDAR particularly valuable for perception applications such as self‐localization, mapping, or object detection. The unstructured nature of 3D LiDAR point clouds, however, coupled with their possibly large size and density, presents a significant hurdle in terms of real‐time capability for these kinds of tasks. Object detection on 3D LiDAR sensor data is therefore challenging and solutions in agriculture usually are tied to very narrow use cases or rigorous down sampling to ensure real‐time applicability. Here, we present an algorithm featuring 2.5D map representations to avoid the computational drawbacks and ensure real‐time capability. We utilize established algorithms to project the 3D LiDAR data into two distinct maps and then apply object detection algorithms on each of those maps individually and to subsequently combine the information into a joint estimate. From the information stored in the 2.5D map, axis‐aligned bounding boxes for each object are computed containing information about position and dimensions. We present a proof of concept and assess the real‐time capability for a domain‐specific use case.

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Zero‐shot obstacle detection using panoramic vision in farmland

Wang,

Chen,

Wang

et al. 2023

Journal of Field Robotics

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Reliable obstacle detection is of great significance to the navigation technology of unmanned agricultural machinery. Currently, most of the previous works have achieved significant performance with the help of visual prior information of obstacles, where visual prior information refers to the visual features learned by models in the training stage. However, collecting enough annotated images for the training stage can be challenging. In the absence of annotated images, the current methods cannot perform optimally. To address the above problem, this paper presents a zero‐shot obstacle detection model based on the You Only Look Once X backbone, introducing the concept of zero‐shot learning into real‐time obstacle detection systems. Specifically, the cascade of encoder and decoder modules is appended to the presented model, and the integration of semantic space‐based classification and anchor‐free localization modules is used for zero‐shot obstacle detection. The feasibility of the proposed method was verified on the actual farmland test data set. The experimental results show that the F1 scores of the seen obstacle (e.g., people) and the unseen obstacle (e.g., agricultural machinery) are 96.22% and 94.66%, respectively. The average detection time for each panoramic image is 52.52 ms (equivalent to 19.04 FPS). The proposed obstacle detection method exhibits superior performance in the situation where training samples of the target category are not available. Notably, the proposed model not only performs the correct classification of unseen obstacles, but also improves the detection performance of both seen and unseen obstacles. The proposed method achieves a balance between accuracy and detection speed.

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Recent developments and applications of simultaneous localization and mapping in agriculture

Cited by 43 publications

References 216 publications

Monocular Visual-Inertial Odometry for Agricultural Environments

Monocular Visual-Inertial Odometry for Agricultural Environments

LiDAR‐based object detection for agricultural robots

Zero‐shot obstacle detection using panoramic vision in farmland

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