Learned Visual Navigation for Under-Canopy Agricultural Robots

Sivakumar, Arun Narenthiran; Modi, Sahil; Gasparino, Mateus Valverde; Che, Ellis,; Velasquez, Andrés Eduardo Baquero; Chowdhary, Girish; Gupta, Saurabh

doi:10.48550/arxiv.2107.02792

Cited by 4 publications

(4 citation statements)

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“…Montalvo et al [15] has developed a system to detect crop rows in maize fields with high weed pressure, hence trying to solve the crop row detection problem in category "e" of the dataset. Sivakumar et al [20] has developed an under canopy navigation robot with a vision based system attempting to address the major variation of appearance between early and late growth stages. Their approach is an attempt to solve the crop row detection problem in the categories "f" and "g" of the dataset.…”

Section: A Data Categoriesmentioning

confidence: 99%

Towards Infield Navigation: leveraging simulated data for crop row detection

Silva¹,

Cielniak²,

Gao³

2022

Preprint

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Agricultural datasets for crop row detection are often bound by their limited number of images. This restricts the researchers from developing deep learning based models for precision agricultural tasks involving crop row detection. We suggest the utilization of small real-world datasets along with additional data generated by simulations to yield similar crop row detection performance as that of a model trained with a large real world dataset. Our method could reach the performance of a deep learning based crop row detection model trained with real-world data by using 60% less labelled realworld data. Our model performed well against field variations such as shadows, sunlight and grow stages. We introduce an automated pipeline to generate labelled images for crop row detection in simulation domain. An extensive comparison is done to analyze the contribution of simulated data towards reaching robust crop row detection in various real-world field scenarios.

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Section: A Data Categoriesmentioning

confidence: 99%

Towards Infield Navigation: leveraging simulated data for crop row detection

Silva¹,

Cielniak²,

Gao³

2022

Preprint

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“…Different modular approaches that use the structure of lanes to learn affordances have been proposed [24], [25]. In agricultural settings, [26] uses crop rows as a reference to train a relative state estimation network to autonomously drive a robot in the lane between two crop rows. However, wooded areas, remote outposts, or other rural landscape does not always have reliable repeating structure.…”

Section: Related Workmentioning

confidence: 99%

WayFAST: Navigation with Predictive Traversability in the Field

Gasparino,

Sivakumar,

Liu

et al. 2022

Preprint

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We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed Way-FAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural network in a self-supervised manner, without requiring heuristics utilized by previous methods. We demonstrate the effectiveness of WayFAST through extensive field testing in varying environments, ranging from sandy dry beaches to forest canopies and snow covered grass fields. Our results clearly demonstrate that WayFAST can learn to avoid geometric obstacles as well as untraversable terrain, such as snow, which would be difficult to avoid with sensors that provide only geometric data, such as LiDAR. Furthermore, we show that our training pipeline based on online traction estimates is more data-efficient than other heuristic-based methods.

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“…Montalvo et al [14] has developed a system to detect crop rows in maize fields with high weed pressure, hence trying to solve the crop row detection problem in category "e" of the dataset. Sivakumar et al [22] has developed an under canopy navigation robot with a vision based system attempting to address the major variation of appearance between early and late growth stages. Their approach is an attempt to solve the crop row detection problem in the categories "f" and "g" of the dataset.…”

Section: B Data Categoriesmentioning

confidence: 99%

Towards agricultural autonomy: crop row detection under varying field conditions using deep learning

Silva¹,

Cielniak²,

Gao³

2021

Preprint

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This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot. A dataset with ten main categories encountered under various field conditions was used for testing. The effect on these conditions on the angular accuracy of crop row detection was compared. A deep convolutional encoder decoder network is implemented to predict crop row masks using RGB input images. The predicted mask is then sent to a post processing algorithm to extract the crop rows. The deep learning model was found to be robust against shadows and growth stages of the crop while the performance was reduced under direct sunlight, increasing weed density, tramlines and discontinuities in crop rows when evaluated with the novel metric.

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Learned Visual Navigation for Under-Canopy Agricultural Robots

Cited by 4 publications

References 0 publications

Towards Infield Navigation: leveraging simulated data for crop row detection

Towards Infield Navigation: leveraging simulated data for crop row detection

WayFAST: Navigation with Predictive Traversability in the Field

Towards agricultural autonomy: crop row detection under varying field conditions using deep learning

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