The training of a next-best-view (NBV) planner for visual place recognition
(VPR) is a fundamentally important task in autonomous robot navigation, for
which a typical approach is the use of visual experiences that are collected in
the target domain as training data. However, the collection of a wide variety
of visual experiences in everyday navigation is costly and prohibitive for
real-time robotic applications. We address this issue by employing a novel {\it
domain-invariant} NBV planner. A standard VPR subsystem based on a
convolutional neural network (CNN) is assumed to be available, and its
domain-invariant state recognition ability is proposed to be transferred to
train the domain-invariant NBV planner. Specifically, we divide the visual cues
that are available from the CNN model into two types: the output layer cue
(OLC) and intermediate layer cue (ILC). The OLC is available at the output
layer of the CNN model and aims to estimate the state of the robot (e.g., the
robot viewpoint) with respect to the world-centric view coordinate system. The
ILC is available within the middle layers of the CNN model as a high-level
description of the visual content (e.g., a saliency image) with respect to the
ego-centric view. In our framework, the ILC and OLC are mapped to a state
vector and subsequently used to train a multiview NBV planner via deep
reinforcement learning. Experiments using the public NCLT dataset validate the
effectiveness of the proposed method.

通过使用深度强化学习训练域不变的 next-best-view planner（下一个最佳视角规划器）来解决在自主机器人导航中收集大量视觉经验的高成本问题。具体来说，使用卷积神经网络中的输出层提示和中间层提示来训练多视角 NBV 规划器并在公共 NCLT 数据集上进行实验证实了该方法的有效性。

使用自身中心和世界中心地图实现主动域无关的自定位

Active Domain-Invariant Self-Localization Using Ego-Centric and World-Centric Maps

State-of-the-art stereo matching networks have difficulties in generalizing
to new unseen environments due to significant domain differences, such as
color, illumination, contrast, and texture. In this paper, we aim at designing
a domain-invariant stereo matching network (DSMNet) that generalizes well to
unseen scenes. To achieve this goal, we propose i) a novel "domain
normalization" approach that regularizes the distribution of learned
representations to allow them to be invariant to domain differences, and ii) a
trainable non-local graph-based filter for extracting robust structural and
geometric representations that can further enhance domain-invariant
generalizations. When trained on synthetic data and generalized to real test
sets, our model performs significantly better than all state-of-the-art models.
It even outperforms some deep learning models (e.g. MC-CNN) fine-tuned with
test-domain data.

本文提出了设计域不变的立体匹配网络的方法，其中使用了新颖的 “域标准化” 方法以及可训练的基于非局部图滤波器的方法，使网络能够在新的场景中泛化，并在实验中得到了显著提高。

域不变立体匹配网络

Domain-invariant Stereo Matching Networks

Recent works showed that Generative Adversarial Networks (GANs) can be
successfully applied in unsupervised domain adaptation, where, given a labeled
source dataset and an unlabeled target dataset, the goal is to train powerful
classifiers for the target samples. In particular, it was shown that a GAN
objective function can be used to learn target features indistinguishable from
the source ones. In this work, we extend this framework by (i) forcing the
learned feature extractor to be domain-invariant, and (ii) training it through
data augmentation in the feature space, namely performing feature augmentation.
While data augmentation in the image space is a well established technique in
deep learning, feature augmentation has not yet received the same level of
attention. We accomplish it by means of a feature generator trained by playing
the GAN minimax game against source features. Results show that both enforcing
domain-invariance and performing feature augmentation lead to superior or
comparable performance to state-of-the-art results in several unsupervised
domain adaptation benchmarks.

本研究使用生成对抗网络（GANs）的目标函数来学习与源数据集不可区分的目标特征，并将其扩展到强制学习通过特征增强在特征空间进行培训的无监督域自适应任务。结果表明，强制进行域不变性和执行特征增强可以导致几个无监督域适应基准测试的优秀或相当的性能。