ArticleSensors (Basel, Switzerland)2021
Obstacle Detection System for Agricultural Mobile Robot Application Using RGB-D Cameras.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Survey of Environmental Perception for Unmanned Ground Agricultural Machinery in Field Environments.Sensors (Basel, Switzerland) · 2026Review
- Crowd navigation in a multi-room environment: a model predictive control framework for mobile robots.Frontiers in robotics and AI · 2026Article
- Research on High-Precision Localization Method of Curved Surface Feature Points Based on RGB-D Data Fusion.Sensors (Basel, Switzerland) · 2025Article
- Outdoor Characterization and Geometry-Aware Error Modelling of an RGB-D Stereo Camera for Safety-Related Obstacle Detection.Sensors (Basel, Switzerland) · 2025Article
- Efficient Learning-Based Robotic Navigation Using Feature-Based RGB-D Pose Estimation and Topological Maps.Entropy (Basel, Switzerland) · 2025Article
- Data Fusion of RGB and Depth Data with Image Enhancement.Journal of imaging · 2024Article
- Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation.Sensors (Basel, Switzerland) · 2023Article
- Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects.Sensors (Basel, Switzerland) · 2023Review
- The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning.Sensors (Basel, Switzerland) · 2022Article
- Framework for environment perception: Ensemble method for vision-based scene understanding algorithms in agriculture.Frontiers in robotics and AI · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Mobile robots designed for agricultural tasks need to deal with challenging outdoor unstructured environments that usually have dynamic and static obstacles. This assumption significantly limits the number of mapping, path planning, and navigation algorithms to be used in this application. As a representative case, the autonomous lawn mowing robot considered in this work is required to determine the working area and to detect obstacles simultaneously, which is a key feature for its working efficiency and safety. In this context, RGB-D cameras are the optimal solution, providing a scene image including depth data with a compromise between precision and sensor cost. For this reason, the obstacle detection effectiveness and precision depend significantly on the sensors used, and the information processing approach has an impact on the avoidance performance. The study presented in this work aims to determine the obstacle mapping accuracy considering both hardware- and information processing-related uncertainties. The proposed evaluation is based on artificial and real data to compute the accuracy-related performance metrics. The results show that the proposed image and depth data processing pipeline introduces an additional distortion of 38 cm.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.