  In this work, we propose a new approach that combines data from multiple
sensors for reliable obstacle avoidance. The sensors include two depth cameras
and a LiDAR arranged so that they can capture the whole 3D area in front of the
robot and a 2D slide around it. To fuse the data from these sensors, we first
use an external camera as a reference to combine data from two depth cameras. A
projection technique is then introduced to convert the 3D point cloud data of
the cameras to its 2D correspondence. An obstacle avoidance algorithm is then
developed based on the dynamic window approach. A number of experiments have
been conducted to evaluate our proposed approach. The results show that the
robot can effectively avoid static and dynamic obstacles of different shapes
and sizes in different environments.
