  For autonomous robots navigating in urban environments, it is important for
the robot to stay on the designated path of travel (i.e., the footpath), and
avoid areas such as grass and garden beds, for safety and social conformity
considerations. This paper presents an autonomous navigation approach for
unknown urban environments that combines the use of semantic segmentation and
LiDAR data. The proposed approach uses the segmented image mask to create a 3D
obstacle map of the environment, from which, the boundaries of the footpath is
computed. Compared to existing methods, our approach does not require a
pre-built map and provides a 3D understanding of the safe region of travel,
enabling the robot to plan any path through the footpath. Experiments comparing
our method with two alternatives using only LiDAR or only semantic segmentation
show that overall our proposed approach performs significantly better with
greater than 91% success rate outdoors, and greater than 66% indoors. Our
method enabled the robot to remain on the safe path of travel at all times, and
reduced the number of collisions.
