This paper deals with the problem of detecting fallen people lying on the
floor by means of a mobile robot equipped with a 3D depth sensor. In the
proposed algorithm, inspired by semantic segmentation techniques, the 3D scene
is over-segmented into small patches. Fallen people are then detected by means
of two SVM classifiers: the first one labels each patch, while the second one
captures the spatial relations between them. This novel approach showed to be
robust and fast. Indeed, thanks to the use of small patches, fallen people in
real cluttered scenes with objects side by side are correctly detected.
Moreover, the algorithm can be executed on a mobile robot fitted with a
standard laptop making it possible to exploit the 2D environmental map built by
the robot and the multiple points of view obtained during the robot navigation.
Additionally, this algorithm is robust to illumination changes since it does
not rely on RGB data but on depth data. All the methods have been thoroughly
validated on the IASLAB-RGBD Fallen Person Dataset, which is published online
as a further contribution. It consists of several static and dynamic sequences
with 15 different people and 2 different environments.

本文介绍了一种基于语义分割的算法，它使用 3D 深度传感器在地面上检测倒下的人，并通过使用 SVM 分类器来实现。该算法可在配备标准笔记本电脑的移动机器人上执行，并在实际情景中显示出鲁棒性和速度。

移动机器人快速和稳健探测倒地人

Fast and Robust Detection of Fallen People from a Mobile Robot

The hearing sense on a mobile robot is important because it is
omnidirectional and it does not require direct line-of-sight with the sound
source. Such capabilities can nicely complement vision to help localize a
person or an interesting event in the environment. To do so the robot auditory
system must be able to work in noisy, unknown and diverse environmental
conditions. In this paper we present a robust sound source localization method
in three-dimensional space using an array of 8 microphones. The method is based
on time delay of arrival estimation. Results show that a mobile robot can
localize in real time different types of sound sources over a range of 3 meters
and with a precision of 3 degrees.

本文介绍使用 8 个麦克风的时间延迟到达估计法在三维空间中实现机器人对不同类型声源的实时定位，能够在 3 米范围内，3 度的精度内快速响应，以补充视觉对环境中感兴趣人或事件的本地化。

移动机器人麦克风阵列的鲁棒声源定位

Robust Sound Source Localization Using a Microphone Array on a Mobile  Robot

Mobile robots in real-life settings would benefit from being able to localize
and track sound sources. Such a capability can help localizing a person or an
interesting event in the environment, and also provides enhanced processing for
other capabilities such as speech recognition. To give this capability to a
robot, the challenge is not only to localize simultaneous sound sources, but to
track them over time. In this paper we propose a robust sound source
localization and tracking method using an array of eight microphones. The
method is based on a frequency-domain implementation of a steered beamformer
along with a particle filter-based tracking algorithm. Results show that a
mobile robot can localize and track in real-time multiple moving sources of
different types over a range of 7 meters. These new capabilities allow a mobile
robot to interact using more natural means with people in real life settings.

本文提出了一种使用八个麦克风阵列实现鲁棒声源定位和跟踪的方法，可在 7 米范围内实时定位和跟踪多种类型的移动声源，从而使移动机器人能够在现实环境中更自然地与人们交互。

使用波束成形和粒子滤波的同时移动声源的鲁棒定位和跟踪

Robust Localization and Tracking of Simultaneous Moving Sound Sources  Using Beamforming and Particle Filtering

We address the problem of controlling a mobile robot to explore a partially
known environment. The robot's objective is the maximization of the amount of
information collected about the environment. We formulate the problem as a
partially observable Markov decision process (POMDP) with an
information-theoretic objective function, and solve it applying forward
simulation algorithms with an open-loop approximation. We present a new
sample-based approximation for mutual information useful in mobile robotics.
The approximation can be seamlessly integrated with forward simulation planning
algorithms. We investigate the usefulness of POMDP based planning for
exploration, and to alleviate some of its weaknesses propose a combination with
frontier based exploration. Experimental results in simulated and real
environments show that, depending on the environment, applying POMDP based
planning for exploration can improve performance over frontier exploration.

本研究针对部分已知环境探索问题，以信息论目标函数为目标，将其视为部分可观察马尔可夫决策过程 (POMDP)，并通过 open-loop 逼近算法求解。提出了新的互信息采样逼近方法用于移动机器人，结果显示 POMDP 探索算法在某些情况下可以提高性能。