  Quadruped locomotion is currently a vibrant research area, which has reached
a level of maturity and performance that enables some of the most advanced
real-world applications with autonomous quadruped robots both in academia and
industry. Blind robust quadruped locomotion has been pushed forward in control
and technology aspects within recent decades. However, in the complicated
environment, the capability including terrain perception and path planning is
still required. Visual perception is an indispensable ability in legged
locomotion for such a demand. This study explores a vision-based navigation
method for a small-scale quadruped robot Pegasus-Mini, aiming to propose a
method that enables efficient and reliable navigation for the small-scale
quadruped locomotion. The vision-based navigation method proposed in this study
is applicable in such a small-scale quadruped robot platform in which the
computation resources and space are limited. The semantic segmentation based on
a CNN model is adopted for the real-time path segmentation in the outdoor
environment. The desired traverse trajectory is generated through real-time
updating the middle line, which is calculated from the edge position of the
segmented path in the images. To enhance the stability of the path planning
directly based on the semantic segmentation method, a trajectory compensation
method is supplemented considering the temporal information to revise the
untrustworthy planned path. Experiments of semantic segmentation and navigation
in a garden scene are demonstrated to verify the effectiveness of the proposed
method.
