深層セマンティックセグメンテーションによる樹列間のGPS不要自律ナビゲーション
GPS-free Autonomous Navigation in Cluttered Tree Rows with Deep Semantic Segmentation
樹冠が空を遮る果樹園でも、合成データのみで学習したセグメンテーションDNNにより、GPSなしでロボットを樹列に沿って誘導する手法を提案し、実環境で検証した。
著者: Alessandro Navone, Mauro Martini, Marco Ambrosio, Andrea Ostuni, Simone Angarano, Marcello Chiaberge
分類: cs.RO
原文アブストラクト
Segmentation-based autonomous navigation has recently been presented as an appealing approach to guiding robotic platforms through crop rows without requiring perfect GPS localization. Nevertheless, current techniques are restricted to situations where the distinct separation between the plants and the sky allows for the identification of the row's center. However, tall, dense vegetation, such as high tree rows and orchards, is the primary cause of GPS signal blockage. In this study, we increase the overall robustness and adaptability of the control algorithm by extending the segmentation-based robotic guiding to those cases where canopies and branches occlude the sky and prevent the utilization of GPS and earlier approaches. An efficient Deep Neural Network architecture has been used to address semantic segmentation, performing the training with synthetic data only. Numerous vineyards and tree fields have undergone extensive testing in both simulation and real-world to show the solution's competitive benefits.
