Service robotics is recently enhancing precision agriculture enabling many
automated processes based on efficient autonomous navigation solutions.
However, data generation and infield validation campaigns hinder the progress
of large-scale autonomous platforms. Simulated environments and deep visual
perception are spreading as successful tools to speed up the development of
robust navigation with low-cost RGB-D cameras. In this context, the
contribution of this work is twofold: a synthetic dataset to train deep
semantic segmentation networks together with a collection of virtual scenarios
for a fast evaluation of navigation algorithms. Moreover, an automatic
parametric approach is developed to explore different field geometries and
features. The simulation framework and the dataset have been evaluated by
training a deep segmentation network on different crops and benchmarking the
resulting navigation.

本文介绍了通过使用合成数据集和虚拟场景来加速自主导航系统的开发的方法及其在农业领域的应用。

加强模拟环境下基于行的作物导航基准测试与感知数据生成

Enhancing Navigation Benchmarking and Perception Data Generation for  Row-based Crops in Simulation

In recent years, precision agriculture has gradually oriented farming closer
to automation processes to support all the activities related to field
management. Service robotics plays a predominant role in this evolution by
deploying autonomous agents that can navigate fields while performing tasks
without human intervention, such as monitoring, spraying, and harvesting. To
execute these precise actions, mobile robots need a real-time perception system
that understands their surroundings and identifies their targets in the wild.
Generalizing to new crops and environmental conditions is critical for
practical applications, as labeled samples are rarely available. In this paper,
we investigate the problem of crop segmentation and propose a novel approach to
enhance domain generalization using knowledge distillation. In the proposed
framework, we transfer knowledge from an ensemble of models individually
trained on source domains to a student model that can adapt to unseen target
domains. To evaluate the proposed method, we present a synthetic multi-domain
dataset for crop segmentation containing plants of variegate shapes and
covering different terrain styles, weather conditions, and light scenarios for
more than 50,000 samples. We demonstrate significant improvements in
performance over state-of-the-art methods. Our approach provides a promising
solution for domain generalization in crop segmentation and has the potential
to enhance precision agriculture applications.

本文提出了一种基于知识蒸馏的新方法以增强领域通用性，在精度农业中进行作物分割的应用，旨在解决移动机器人在野外环境中识别和定位作物所需的实时感知问题。通过在一个有多个领域的合成数据集上评估，该方法取得了明显的表现改进。

知识蒸馏用于农作物分割的领域泛化

Domain Generalization for Crop Segmentation with Knowledge Distillation

The development of precision agriculture has gradually introduced automation
in the agricultural process to support and rationalize all the activities
related to field management. In particular, service robotics plays a
predominant role in this evolution by deploying autonomous agents able to
navigate in fields while executing different tasks without the need for human
intervention, such as monitoring, spraying and harvesting. In this context,
global path planning is the first necessary step for every robotic mission and
ensures that the navigation is performed efficiently and with complete field
coverage. In this paper, we propose a learning-based approach to tackle
waypoint generation for planning a navigation path for row-based crops,
starting from a top-view map of the region-of-interest. We present a novel
methodology for waypoint clustering based on a contrastive loss, able to
project the points to a separable latent space. The proposed deep neural
network can simultaneously predict the waypoint position and cluster assignment
with two specialized heads in a single forward pass. The extensive
experimentation on simulated and real-world images demonstrates that the
proposed approach effectively solves the waypoint generation problem for both
straight and curved row-based crops, overcoming the limitations of previous
state-of-the-art methodologies.

本研究提出了一种基于学习的方法，针对行作物的路径生成问题，通过对区域地图的聚类方法，生成导航路径指向航路点，验证结果表明该方法可以高效地解决路径生成问题并超越之前的模型。

基于深度学习和对比聚类的行作物路径点生成

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

Robots operating in domestic environments generally need to interact with
articulated objects, such as doors, cabinets, dishwashers or fridges. In this
work, we present a novel, probabilistic framework for modeling articulated
objects as kinematic graphs. Vertices in this graph correspond to object parts,
while edges between them model their kinematic relationship. In particular, we
present a set of parametric and non-parametric edge models and how they can
robustly be estimated from noisy pose observations. We furthermore describe how
to estimate the kinematic structure and how to use the learned kinematic models
for pose prediction and for robotic manipulation tasks. We finally present how
the learned models can be generalized to new and previously unseen objects. In
various experiments using real robots with different camera systems as well as
in simulation, we show that our approach is valid, accurate and efficient.
Further, we demonstrate that our approach has a broad set of applications, in
particular for the emerging fields of mobile manipulation and service robotics.

该研究提出了一种用于模拟动态图的概率框架，包括对边模型的参数化和非参数模型的建模，以及如何估算物体运动的结构和运动学模型以及其在机器人操作和移动操作中的应用。