Recognizing already explored places (a.k.a. place recognition) is a
fundamental task in Simultaneous Localization and Mapping (SLAM) to enable
robot relocalization and loop closure detection. In topological SLAM the
recognition takes place by comparing a signature (or feature vector) associated
to the current node with the signatures of the nodes in the known map. However,
as the number of nodes increases, matching the current node signature against
all the existing ones becomes inefficient and thwarts real-time navigation. In
this paper we propose a novel approach to pre-select a subset of map nodes for
place recognition. The map nodes are clustered during exploration and each
cluster is associated with a region. The region labels become the prediction
targets of a deep neural network and, during navigation, only the nodes
associated with the regions predicted with high probability are considered for
matching. While the proposed technique can be integrated in different SLAM
approaches, in this work we describe an effective integration with RTAB-Map (a
popular framework for real-time topological SLAM) which allowed us to design
and run several experiments to demonstrate its effectiveness. All the code and
material from the experiments will be available online at
this https URL.

本文提出了一种预选地图节点进行场所识别的新方法，并将其与 RTAB-Map 集成，证明了其在实时导航中的有效性。

针对大地图的高效机器人定位的区域预测

Region Prediction for Efficient Robot Localization on Large Maps

We suggest to represent an X-Field -a set of 2D images taken across different
view, time or illumination conditions, i.e., video, light field, reflectance
fields or combinations thereof-by learning a neural network (NN) to map their
view, time or light coordinates to 2D images. Executing this NN at new
coordinates results in joint view, time or light interpolation. The key idea to
make this workable is a NN that already knows the "basic tricks" of graphics
(lighting, 3D projection, occlusion) in a hard-coded and differentiable form.
The NN represents the input to that rendering as an implicit map, that for any
view, time, or light coordinate and for any pixel can quantify how it will move
if view, time or light coordinates change (Jacobian of pixel position with
respect to view, time, illumination, etc.). Our X-Field representation is
trained for one scene within minutes, leading to a compact set of trainable
parameters and hence real-time navigation in view, time and illumination.

本文提出了一种使用神经网络学习视图、时间或光照坐标与二维图像之间映射关系的方法，可以实现实时的多视图、多时间和多光照插值，其 X-Field 表达式在几分钟内可以训练成功，参数集紧凑，可在视图、时间和光照导航中进行实时操作。

X-Fields: 隐式神经视图、光和时间影像插值

X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation

Lane detection algorithms have been the key enablers for a fully-assistive
and autonomous navigation systems. In this paper, a novel and pragmatic
approach for lane detection is proposed using a convolutional neural network
(CNN) model based on SegNet encoder-decoder architecture. The encoder block
renders low-resolution feature maps of the input and the decoder block provides
pixel-wise classification from the feature maps. The proposed model has been
trained over 2000 image data-set and tested against their corresponding
ground-truth provided in the data-set for evaluation. To enable real-time
navigation, we extend our model's predictions interfacing it with the existing
Google APIs evaluating the metrics of the model tuning the hyper-parameters.
The novelty of this approach lies in the integration of existing segNet
architecture with google APIs. This interface makes it handy for assistive
robotic systems. The observed results show that the proposed method is robust
under challenging occlusion conditions due to pre-processing involved and gives
superior performance when compared to the existing methods.

本文提出了一种基于卷积神经网络和 SegNet encoder-decoder 架构的新型车道检测方法，并通过与 Google API 的接口实现了实时导航，该方法针对挑战性遮挡条件具有鲁棒性，且性能优于现有方法。

使用谷歌街景和 CNN 进行车道检测的动态方法

Dynamic Approach for Lane Detection using Google Street View and CNN

We present a novel approach to automatically identify driver behaviors from
vehicle trajectories and use them for safe navigation of autonomous vehicles.
We propose a novel set of features that can be easily extracted from car
trajectories. We derive a data-driven mapping between these features and six
driver behaviors using an elaborate web-based user study. We also compute a
summarized score indicating a level of awareness that is needed while driving
next to other vehicles. We also incorporate our algorithm into a vehicle
navigation simulation system and demonstrate its benefits in terms of safer
real-time navigation, while driving next to aggressive or dangerous drivers.

提出一种新方法，从车辆轨迹自动识别驾驶员行为，使用这些行为进行自动驾驶车辆的安全导航，该方法包括从轨迹中提取一系列特征，通过一个网络用户研究，将这些特征与六种驾驶员行为建立数据驱动的映射，并计算评分，我们把算法应用于车辆导航仿真系统，并展示了它在和危险驾驶员或咄咄逼人的驾驶员并行行驶时的安全实时导航方面的优势。