  This paper addresses the problem of Human-Aware Navigation (HAN), using multi
camera sensors to implement a vision-based person tracking system. The main
contributions of this paper are as follows: a novel and efficient Deep Learning
person detection and a standardization of human-aware constraints. In the first
stage of the approach, we propose to cascade the Aggregate Channel Features
(ACF) detector with a deep Convolutional Neural Network (CNN) to achieve fast
and accurate Pedestrian Detection (PD). Regarding the human awareness (that can
be defined as constraints associated with the robot's motion), we use a mixture
of asymmetric Gaussian functions, to define the cost functions associated to
each constraint. Both methods proposed herein are evaluated individually to
measure the impact of each of the components. The final solution (including
both the proposed pedestrian detection and the human-aware constraints) is
tested in a typical domestic indoor scenario, in four distinct experiments. The
results show that the robot is able to cope with human-aware constraints,
defined after common proxemics and social rules.
