A Vision-Based Navigation System for Arable Fields
Vision-based navigation systems in arable fields are an underexplored area in agricultural robot navigation. Vision systems deployed in arable fields face challenges such as fluctuating weed density, varying illumination levels, growth stages and crop row irregularities. Current solutions are often crop-specific and aimed to address limited individual conditions such as illumination or weed density. Moreover, the scarcity of comprehensive datasets hinders the development of generalised machine learning systems for navigating these fields. This paper proposes a suite of deep learning-based perception algorithms using affordable vision sensors for vision-based navigation in arable fields. Initially, a comprehensive dataset that captures the intricacies of multiple crop seasons, various crop types, and a range of field variations was compiled. Next, this study delves into the creation of robust infield perception algorithms capable of accurately detecting crop rows under diverse conditions such as different growth stages, weed density, and varying illumination. Further, it investigates the integration of crop row following with vision-based crop row switching for efficient field-scale navigation. The proposed infield navigation system was tested in commercial arable fields traversing a total distance of 4.5 km with average heading and cross-track errors of 1.24{\deg} and 3.32 cm respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationNavigateRobot NavigationSemantic SegmentationSimilar Papers 제목 키워드 기반
Towards Autonomous Crop-Agnostic Visual Navigation in Arable Fields
Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring to weed management and fertilizer application. Many current approaches rely on accurate GPS, however, such technolo…
Autonomous NavigationManagementVisual NavigationDetect and Approach: Close-Range Navigation Support for People with Blindness and Low Vision
People with blindness and low vision (pBLV) experience significant challenges when locating final destinations or targeting specific objects in unfamiliar environments. Furthermore, besides initially locating and orienti…
ObjectObject LocalizationVision based Crop Row Navigation under Varying Field Conditions in Arable Fields
Accurate crop row detection is often challenged by the varying field conditions present in real-world arable fields. Traditional colour based segmentation is unable to cater for all such variations. The lack of comprehen…
NavigateDeep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
Autonomous navigation in agricultural environments is challenged by varying field conditions that arise in arable fields. State-of-the-art solutions for autonomous navigation in such environments require expensive hardwa…
Autonomous NavigationSim-to-Real Transfer via 3D Feature Fields for Vision-and-Language Navigation
Vision-and-language navigation (VLN) enables the agent to navigate to a remote location in 3D environments following the natural language instruction. In this field, the agent is usually trained and evaluated in the navi…
NavigateVision and Language Navigation