11 citations · 22 across the 9 of their papers we have counts for
10 papers
Enabling 24-hour Agricultural Robotics: Unsupervised Day-to-Night Cross-Modal Image Translation for Nighttime Visual Navigation
Robel Mamo, Rajitha de Silva, Grzegorz Cielniak +1
While visual navigation has been extensively studied in agricultural robotics, most existing systems assume daytime conditions. In fact, deploying autonomous robots at night offers…
Semantics-Guided Multimodal Masked Autoencoder Pretraining for 3D BEV Object Detection
Prabuddhi Wariyapperuma, Rajitha de Silva, Marc Hanheide +2
Accurate 3D bird's-eye view (BEV) object detection is essential for autonomous driving, and depends strongly on effective multimodal representations from complementary sensors such…
Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments
Rajitha de Silva, Grzegorz Cielniak
Reliable multi-modal calibration requires identifying which observations truly constrain the extrinsic parameters and which ones mainly add noise or ambiguity. In this paper, we pr…
SPARROW: Smart Precision Agriculture Robot for Ridding of Weeds
Dhanushka Balasingham, Sadeesha Samarathunga, Gayantha Godakanda Arachchige +5
The advancements in precision agriculture are vital to support the increasing demand for global food supply. Precision spot spraying is a major step towards reducing chemical usage…
A Vision-Based Navigation System for Arable Fields
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
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 fluctu…
Leaving the Lines Behind: Vision-Based Crop Row Exit for Agricultural Robot Navigation
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
Usage of purely vision based solutions for row switching is not well explored in existing vision based crop row navigation frameworks. This method only uses RGB images for local fe…
Vision based Crop Row Navigation under Varying Field Conditions in Arable Fields
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
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 al…
Deep learning-based Crop Row Detection for Infield Navigation of Agri-Robots
Rajitha de Silva, Grzegorz Cielniak, Gang Wang +1
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 su…
Towards Infield Navigation: leveraging simulated data for crop row detection
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
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 precis…
Towards agricultural autonomy: crop row detection under varying field conditions using deep learning
Rajitha de Silva, Grzegorz Cielniak, Junfeng Gao
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 encoun…