A Sliding Window for Path Mapping Based on a Pseudo-Derivative Method in Autonomous Navigation

Bentley, Landon; MacInnes, Joe; Mason, Hannah G.; Bhadani, Rahul; Bose, T.

doi:10.1109/vtcfall.2019.8891126

Cited by 2 publications

(1 citation statement)

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“…In urban scenarios, the discontinuous lanes are handled by sliding window algorithm [ 22 ] where the search windows slide in increments for fitting the driving regions. Similarly, in agricultural cases, blobs [ 23 ], or clusters of pixels are used for segmenting the crop regions.…”

Section: Related Workmentioning

confidence: 99%

Autonomous Crop Row Guidance Using Adaptive Multi-ROI in Strawberry Fields

Ponnambalam

Bakken

Moore

et al. 2020

Sensors

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Automated robotic platforms are an important part of precision agriculture solutions for sustainable food production. Agri-robots require robust and accurate guidance systems in order to navigate between crops and to and from their base station. Onboard sensors such as machine vision cameras offer a flexible guidance alternative to more expensive solutions for structured environments such as scanning lidar or RTK-GNSS. The main challenges for visual crop row guidance are the dramatic differences in appearance of crops between farms and throughout the season and the variations in crop spacing and contours of the crop rows. Here we present a visual guidance pipeline for an agri-robot operating in strawberry fields in Norway that is based on semantic segmentation with a convolution neural network (CNN) to segment input RGB images into crop and not-crop (i.e., drivable terrain) regions. To handle the uneven contours of crop rows in Norway’s hilly agricultural regions, we develop a new adaptive multi-ROI method for fitting trajectories to the drivable regions. We test our approach in open-loop trials with a real agri-robot operating in the field and show that our approach compares favourably to other traditional guidance approaches.

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Section: Related Workmentioning

confidence: 99%